Table of contents
Preface
This project is based on publicly available data sets on Kaggle and aims to conduct in-depth feature screening and extraction of heart disease and chronic kidney disease. It utilizes a random forest machine learning model that is trained on these features to predict whether one has these diseases. Not only that, the project will also provide relevant drug recommendations based on the patient's symptoms or needs, thus realizing a highly practical intelligent medical assistant.
First, the project collected public data sets from Kaggle, which contain rich information related to heart disease and chronic kidney disease. Then, through data preprocessing and feature engineering, the most relevant features are extracted from these data for training of machine learning models.
Next, the project employed a random forest machine learning model, a powerful classification algorithm. By using training data, the model is able to learn how different features are associated with heart disease and chronic kidney disease. Once the model is trained, it can make predictions on new patient data to determine whether the patient has these diseases.
In addition to disease prediction, the project also features a drug recommendation system. Based on the patient's symptoms, needs and disease diagnosis, the system will recommend appropriate medications and treatment options to provide more comprehensive medical support.
Taken together, this project can not only predict heart disease and chronic kidney disease, but also provide personalized treatment recommendations. This kind of intelligent medical assistant is expected to improve the accuracy of medical decision-making, provide patients with a better medical experience, and play a positive role in the reasonable allocation of medical resources.
overall design
This part includes the overall system structure diagram and system flow chart .
Operating environment
Python environment
Python 3.6 and above configuration is required. In Windows environment, it is recommended to download Anaconda to complete the configuration of the environment required for Python. The download address is https://www.anaconda.com/ . You can also download a virtual machine to run the code in Linux environment.
Dependent libraries
Use the following command to install:
pip install pandas
Module implementation
This project includes 2 functions, each function has 3 modules: disease prediction, drug recommendation, and module application. The function introduction and related codes of each module are given below.
1. Disease prediction
This module is a small health prediction system that predicts two diseases: heart disease and chronic kidney disease .
2. Drug recommendations
This module is a small drug recommendation system that provides drug recommendations for more than 800 symptoms .
3. Model testing
This section includes model import and related code.
1) Model import
The input data includes two parts: gender, age, and appetite require manual input by the user; heart rate and electrocardiogram waveform parameters require the user to access different sensors for measurement. Considering the convenience of application, all parameters are directly read from the sensor for prediction.
The data input by a user is shown in Figure 11, and the judgment result is shown in Figure 12.
client_result=rf.predict(client_x)
print('这就是分类预测结果')
print(client_result )
Based on the data and model, first determine whether the disease is disease or not; secondly, determine the severity of the disease. You cannot just use whether you are sick or not, but give different levels of evaluation to the condition. Multiply the data by the weight of each factor and compare it to the average for people with the disease. If you tell a person directly that you are sick, you may not be able to accept it. If it shows that the condition is not too serious and is lighter than most patients, it will be easier to accept after quantification,
as shown in the picture.
The previous average was that patients were smaller than normal, so it was better to be larger than the interface. The disease index is less than a quarter of the average. After quantification, patients are urged to seize the time for treatment.
2) Related code
This part includes model prediction code, model application innovation code, user interface and interface visualization code.
(1) Model prediction
Heart disease prediction model modeling, the relevant code is as follows:
#心脏病预测模型建模
#导入所用库函数及数据集
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import os
#读入数据集中数据
df = pd.read_csv("C:/Users/Administrator/Desktop/dasanxia/Thursday9 10 11/heart.csv")
#输出读入的数据
df.head()
#检查是否有缺省值
df.loc[(df['age'].isnull()) |
(df['sex'].isnull()) |
(df['cp'].isnull()) |
(df['trestbps'].isnull()) |
(df['chol'].isnull()) |
(df['fbs'].isnull()) |
(df['restecg'].isnull()) |
(df['thalach'].isnull()) |
(df['exang'].isnull()) |
(df['oldpeak'].isnull()) |
(df['slope'].isnull()) |
(df['ca'].isnull()) |
(df['target'].isnull())]
#通过绘图方式观察数据，能够更好的观察是否有错误
sns.pairplot(df.dropna(), hue='target')
#对血液中胆固醇含量绘制数据分布图
df['chol'].hist()
#对静息血压绘制数据分布图
df['trestbps'].hist()
#将类别变量转换为伪变量
a = pd.get_dummies(df['cp'], prefix = "cp")
b = pd.get_dummies(df['thal'], prefix = "thal")
c = pd.get_dummies(df['slope'], prefix = "slope")
frames = [df, a, b, c]
df = pd.concat(frames, axis = 1)
#数据集可视化
df.head()
#将原来的类别变量删掉，只保留伪变量
df = df.drop(columns = ['cp', 'thal', 'slope'])
#数据集可视化
df.head()
#数据预处理完成，选择、训练并保存模型
#target是标签
y = df.target.values
x_data = df.drop(['target'], axis = 1) #丢下最后一行target
#划分训练集与测试集
x_train, x_test, y_train, y_test = train_test_split(x_data,y,test_size = 0.2,random_state=0)
x_train = x_train.T
y_train = y_train.T
x_test = x_test.T
y_test = y_test.T
#创建数组，带入随机森林模型中迭代
import numpy as np
num=np.zeros(20,int)
for i in range(0,20):
num[i]=i+990
print(num)
#寻找最优参数
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
tuned_parameters = [{
'n_estimators':num,
'class_weight':[None,{
0: 0.33,1:0.67},'balanced'],'random_state':[1]}]
rf = GridSearchCV(RandomForestClassifier(), tuned_parameters, cv=10,scoring='f1')
rf.fit(x_train.T, y_train.T)
#输出找到的最优参数
print('Best parameters:')
print(rf.best_params_)
rf_best = rf.best_estimator_
#带入最优参数的随机森林模型
accuracies = {
}
rf_best.fit(x_train.T, y_train.T)
acc = rf.score(x_test.T,y_test.T)*100
accuracies['Random Forest'] = acc
#输出模型准确率
print("Random Forest Algorithm Accuracy Score : {:.2f}%".format(acc))
#绘制混淆矩阵
y_head_rf = rf_best.predict(x_test.T)
from sklearn.metrics import confusion_matrix
cm_rf = confusion_matrix(y_test,y_head_rf)
#图像大小4*4
plt.figure(figsize=(4,4))
plt.title("Random Forest Confusion Matrix")
sns.heatmap(cm_rf,annot=True,cmap="Blues",fmt="d",cbar=False, annot_kws={
"size": 24})
plt.show()
#绘制ROC曲线
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_head_rf)
fig, ax = plt.subplots()
ax.plot(fpr, tpr)
ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls="--", c=".3")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.rcParams['font.size'] = 12
plt.title('ROC curve for diabetes classifier')
plt.xlabel('False Positive Rate (1 - Specificity)')
plt.ylabel('True Positive Rate (Sensitivity)')
plt.grid(True)
#ROC曲线图的面积
auc(fpr, tpr)
Chronic kidney disease data set training model modeling, the relevant code is as follows:
#慢性肾病数据集训练模型建模
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report,accuracy_score
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')
#%matplotlib inline
#读入数据集并可视化
df = pd.read_csv('C:/Users/Administrator/Desktop/dasanxia/Thursday9 10 11/kidney_disease.csv')
df.head()
#数据预处理，将类别变量转换为伪变量
df[['htn','dm','cad','pe','ane']] = df[['htn','dm','cad','pe','ane']].replace(to_replace={
'yes':1,'no':0})
df[['rbc','pc']]=df[['rbc','pc']].replace(to_replace={
'abnormal':1,'normal':0})
df[['pcc','ba']]=df[['pcc','ba']].replace(to_replace={
'present':1,'notpresent':0})
df[['appet']]=df[['appet']].replace(to_replace={
'good':1,'poor':0,'no':np.nan})
df['classification']=df['classification'].replace(to_replace={
'ckd':1.0,'ckd\t':1.0,'notckd':0.0,'no':0.0})
df.rename(columns={
'classification':'class'},inplace=True)
#进一步清洗
df['pe'] = df['pe'].replace(to_replace='good',value=0)
df['appet']=df['appet'].replace(to_replace='no',value=0)
df['cad']=df['cad'].replace(to_replace='\tno',value=0)
df['dm']=df['dm'].replace(to_replace={
'\tno':0,'\tyes':1,' yes':1, '':np.nan})
df.drop('id',axis=1,inplace=True)
df.head()
#列出所有null的数据
df.loc[(df['age'].isnull()) |
(df['bp'].isnull()) |
(df['sg'].isnull()) |
(df['al'].isnull()) |
(df['su'].isnull()) |
(df['rbc'].isnull()) |
(df['pc'].isnull()) |
(df['pcc'].isnull()) |
(df['ba'].isnull()) |
(df['bgr'].isnull()) |
(df['bu'].isnull()) |
(df['sc'].isnull()) |
(df['sod'].isnull()) |
(df['pot'].isnull()) |
(df['hemo'].isnull()) |
(df['htn'].isnull()) |
(df['dm'].isnull()) |
(df['cad'].isnull()) |
(df['appet'].isnull()) |
(df['pe'].isnull()) |
(df['ane'].isnull()) |
(df['class'].isnull())]
#出现空缺值，采用均值归一法，填补缺失值
#病人均值
average0_age = df.loc[df['class'] ==True, 'age'].mean()
average0_bp = df.loc[df['class'] == True, 'bp'].mean()
average0_sg = df.loc[df['class'] == True, 'sg'].mean()
average0_al = df.loc[df['class'] == True, 'al'].mean()
average0_su = df.loc[df['class'] == True, 'su'].mean()
average0_rbc = df.loc[df['class'] == True, 'rbc'].mean()
average0_pc = df.loc[df['class'] == True, 'pc'].mean()
average0_pcc = df.loc[df['class'] == True, 'pcc'].mean()
average0_ba = df.loc[df['class'] == True, 'ba'].mean()
average0_bgr = df.loc[df['class'] == True, 'bgr'].mean()
average0_bu = df.loc[df['class'] == True, 'bu'].mean()
average0_sc = df.loc[df['class'] == True, 'sc'].mean()
average0_sod = df.loc[df['class'] == True, 'sod'].mean()
average0_pot = df.loc[df['class'] == True, 'pot'].mean()
average0_hemo = df.loc[df['class'] == True, 'hemo'].mean()
average0_htn = df.loc[df['class'] == True, 'htn'].mean()
average0_dm = df.loc[df['class'] == True, 'dm'].mean()
average0_cad = df.loc[df['class'] == True, 'cad'].mean()
average0_appet = df.loc[df['class'] ==True, 'appet'].mean()
average0_pe = df.loc[df['class'] == True, 'pe'].mean()
average0_ane = df.loc[df['class'] == True, 'ane'].mean()
#正常人均值
average1_age = df.loc[df['class'] == False, 'age'].mean()
average1_bp = df.loc[df['class'] == False, 'bp'].mean()
average1_sg = df.loc[df['class'] == False, 'sg'].mean()
average1_al = df.loc[df['class'] == False, 'al'].mean()
average1_su = df.loc[df['class'] == False, 'su'].mean()
average1_rbc = df.loc[df['class'] == False, 'rbc'].mean()
average1_pc = df.loc[df['class'] == False, 'pc'].mean()
average1_pcc = df.loc[df['class'] == False, 'pcc'].mean()
average1_ba = df.loc[df['class'] == False, 'ba'].mean()
average1_bgr = df.loc[df['class'] == False, 'bgr'].mean()
average1_bu = df.loc[df['class'] == False, 'bu'].mean()
average1_sc = df.loc[df['class'] == False, 'sc'].mean()
average1_sod = df.loc[df['class'] == False, 'sod'].mean()
average1_pot = df.loc[df['class'] == False, 'pot'].mean()
average1_hemo = df.loc[df['class'] == False, 'hemo'].mean()
average1_htn = df.loc[df['class'] == False, 'htn'].mean()
average1_dm = df.loc[df['class'] == False, 'dm'].mean()
average1_cad = df.loc[df['class'] == False, 'cad'].mean()
average1_appet = df.loc[df['class'] == False, 'appet'].mean()
average1_pe = df.loc[df['class'] == False, 'pe'].mean()
average1_ane = df.loc[df['class'] == False, 'ane'].mean()
#如果为null，则取均值
df.loc[(df['class'] ==True) &(df['age'].isnull()),'age'] = average0_age
df.loc[(df['class'] ==True) &(df['bp'].isnull()),'bp'] = average0_bp
df.loc[(df['class'] ==True) &(df['sg'].isnull()),'sg'] = average0_sg
df.loc[(df['class'] ==True) &(df['al'].isnull()),'al'] = average0_al
df.loc[(df['class'] ==True) &(df['su'].isnull()),'su'] = average0_su
df.loc[(df['class'] ==True) &(df['rbc'].isnull()),'rbc'] = average0_rbc
df.loc[(df['class'] ==True) &(df['pc'].isnull()),'pc'] = average0_pc
df.loc[(df['class'] ==True) &(df['pcc'].isnull()),'pcc'] = average0_pcc
df.loc[(df['class'] ==True) &(df['ba'].isnull()),'ba'] = average0_ba
df.loc[(df['class'] ==True) &(df['bgr'].isnull()),'bgr'] = average0_bgr
df.loc[(df['class'] ==True) &(df['bu'].isnull()),'bu'] = average0_bu
df.loc[(df['class'] ==True) &(df['sc'].isnull()),'sc'] = average0_sc
df.loc[(df['class'] ==True) &(df['sod'].isnull()),'sod'] = average0_sod
df.loc[(df['class'] ==True) &(df['pot'].isnull()),'pot'] = average0_pot
df.loc[(df['class'] ==True) &(df['hemo'].isnull()),'hemo']=average0_hemo
df.loc[(df['class'] ==True) &(df['htn'].isnull()),'htn'] = average0_htn
df.loc[(df['class'] ==True) &(df['dm'].isnull()),'dm'] = average0_dm
df.loc[(df['class'] ==True) &(df['cad'].isnull()),'cad'] = average0_cad
df.loc[(df['class'] ==True) &(df['appet'].isnull()),'appet'] = average0_appet
df.loc[(df['class'] ==True)&(df['pe'].isnull()),'pe'] = average0_pe
df.loc[(df['class'] ==True) &(df['ane'].isnull()),'ane'] = average0_ane
df.loc[(df['class'] ==False) &(df['age'].isnull()),'age'] = average1_age
df.loc[(df['class'] ==False) &(df['bp'].isnull()),'bp'] = average1_bp
df.loc[(df['class'] ==False) &(df['sg'].isnull()),'sg'] = average1_sg
df.loc[(df['class'] ==False) &(df['al'].isnull()),'al'] = average1_al
df.loc[(df['class'] ==False) &(df['su'].isnull()),'su'] = average1_su
df.loc[(df['class'] ==False) &(df['rbc'].isnull()),'rbc'] = average1_rbc
df.loc[(df['class'] ==False) &(df['pc'].isnull()),'pc'] = average1_pc
df.loc[(df['class'] ==False) &(df['pcc'].isnull()),'pcc'] = average1_pcc
df.loc[(df['class'] ==False) &(df['ba'].isnull()),'ba'] = average1_ba
df.loc[(df['class'] ==False) &(df['bgr'].isnull()),'bgr'] = average1_bgr
df.loc[(df['class'] ==False) &(df['bu'].isnull()),'bu'] = average1_bu
df.loc[(df['class'] ==False) &(df['sc'].isnull()),'sc'] = average1_sc
df.loc[(df['class'] ==False) &(df['sod'].isnull()),'sod'] = average1_sod
df.loc[(df['class'] ==False) &(df['pot'].isnull()),'pot'] = average1_pot
df.loc[(df['class'] ==False) &(df['hemo'].isnull()),'hemo'] = average1_hemo
df.loc[(df['class'] ==False) &(df['htn'].isnull()),'htn'] = average1_htn
df.loc[(df['class'] ==False) &(df['dm'].isnull()),'dm'] = average1_dm
df.loc[(df['class'] ==False) &(df['cad'].isnull()),'cad'] = average1_cad
df.loc[(df['class'] ==False) &(df['appet'].isnull()),'appet'] = average1_appet
df.loc[(df['class'] ==False) &(df['pe'].isnull()),'pe'] = average1_pe
df.loc[(df['class'] ==False) &(df['ane'].isnull()),'ane'] = average1_ane
#重新检查是否有缺省值
df.loc[(df['age'].isnull()) |
(df['bp'].isnull()) |
(df['sg'].isnull()) |
(df['al'].isnull()) |
(df['su'].isnull()) |
(df['rbc'].isnull()) |
(df['pc'].isnull()) |
(df['pcc'].isnull()) |
(df['ba'].isnull()) |
(df['bgr'].isnull()) |
(df['bu'].isnull()) |
(df['sc'].isnull()) |
(df['sod'].isnull()) |
(df['pot'].isnull()) |
(df['hemo'].isnull()) |
(df['htn'].isnull()) |
(df['dm'].isnull()) |
(df['cad'].isnull()) |
(df['appet'].isnull()) |
(df['pe'].isnull()) |
(df['ane'].isnull()) |
(df['class'].isnull())]
#划分训练集测试集
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,:-1], df['class'], test_size = 0.33, random_state=44,stratify= df['class'] )
print(X_train.shape)
print(X_test.shape)
#寻找随机森林最优参数
tuned_parameters = [{
'n_estimators':[7,8,9,10,11,12,13,14,15,16],'max_depth':[2,3,4,5,6,None],'class_weight':[None,{
0: 0.33,1:0.67},'balanced'],'random_state':[42]}]
clf = GridSearchCV(RandomForestClassifier(), tuned_parameters, cv=10,scoring='f1')
clf.fit(X_train, y_train)
#输出最佳参数
print('Best parameters:')
print(clf.best_params_)
clf_best = clf.best_estimator_
#将最优参数代入随机森林模型
accuracies = {
}
rf = RandomForestClassifier(class_weight=None, max_depth= 6,n_estimators = 7, random_state = 42)
rf.fit(X_train, y_train)
#计算模型准确率
acc = rf.score(X_test, y_test)*100
accuracies['Random Forest'] = acc
print("Random Forest Algorithm Accuracy Score : {:.2f}%".format(acc))
Drug review sentiment analysis modeling, the relevant code is as follows:
#药物评论情感分析建模
#导入库函数
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style; style.use('ggplot')
import re
import xgboost as xgb
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
from sklearn.naive_bayes import MultinomialNB, GaussianNB
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_selection import chi2, SelectKBest
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from wordcloud import WordCloud, STOPWORDS
from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding
from keras.utils import to_categorical
#读入训练集和测试集
train = pd.read_csv('C:/Users/Administrator/Desktop/dasanxia/Thursday9 10 11/drugsComTrain_raw.csv')
test = pd.read_csv('C:/Users/Administrator/Desktop/dasanxia/Thursday9 10 11/drugsComTest_raw.csv')
#输出可视化
print('全部评论数：')
print(len(train))
print(len(test))
#仅保留评分为1和10分两端的评论
X_train=train[train.rating.isin([1,10])]
X_train.head()
X_test=test[test.rating.isin([1,10])]
X_test.head()
#编写去除评论中引号的函数
def remove_enclosing_quotes(s):
if s[0] == '"' and s[-1] == '"':
return s[1:-1]
else:
return s
#调用函数
train.review = train.review.apply(remove_enclosing_quotes)
test.review = test.review.apply(remove_enclosing_quotes)
#用正则表达式去除乱码，防止对后续分隔句子造成影响
import re
train.review = train.review.apply(lambda x: re.sub(r'&#\d+;',r'', x))
test.review = test.review.apply(lambda x: re.sub(r'&#\d+;',r'', x))
#编写函数，将症状，药物写进评论中，拼成一个整体
def combine_text_columns(data_frame, text_cols):
text_data = data_frame[text_cols]
text_data.fillna("", inplace=True)
return text_data.apply(lambda x: " ".join(x), axis=1)
#调用函数
text_cols = ['drugName', 'condition', 'review']
train['text'] = combine_text_columns(train, text_cols)
test['text'] = combine_text_columns(test, text_cols)
#过滤规则，token的正则表达式
TOKENS_ALPHANUMERIC = '[A-Za-z0-9]+(?=\\s+)'
#CountVectorizer 类将文本中的词语转换为词频矩阵
vec_alphanumeric = CountVectorizer(token_pattern=TOKENS_ALPHANUMERIC, ngram_range=(1,2), lowercase=True, stop_words='english', min_df=2, max_df=0.99)
#转换transform，从而实现数据的标准化、归一化
X = vec_alphanumeric.fit_transform(train.text)
#将1和10分评论二分类，归位两堆
train['binary_rating'] = train['rating'] > 5
y = train.binary_rating
#划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y, test_size=0.1)
#逻辑回归训练模型
clf_lr = LogisticRegression(penalty='l2', C=100).fit(X_train, y_train)
pred = clf_lr.predict(X_test)
#模型在训练集准确率
print("Accuracy on training set: {}".format(clf_lr.score(X_train, y_train)))
#模型在测试集准确率
print("Accuracy on test set: {}".format(clf_lr.score(X_test, y_test)))
#绘制混淆矩阵
print("Confusion Matrix")
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred))
(2) Model application innovation
The relevant code is as follows:
#得到心脏病数据集中特征重要性
import eli5
from eli5.sklearn import PermutationImportance
perm = PermutationImportance(rf, random_state=1).fit(x_test.T, y_test.T)
eli5.show_weights(perm, feature_names = x_test.T.columns.tolist())
#得到正常人和患者均值
df.groupby('target').mean()
#正常人平均水平
average0_count=np.multiply(average0,w)
average0_sum=sum(average0_count)
#病人平均水平
average1_count=np.multiply(average1,w)
average1_sum=sum(average1_count)
#得到慢性肾病数据集中特征重要性
import eli5 #for purmutation importance
from eli5.sklearn import PermutationImportance
perm = PermutationImportance(rf, random_state=1).fit(X_test, y_test)
eli5.show_weights(perm, feature_names = X_test.columns.tolist())
df.groupby('class').mean()
#病人平均水平
average0_count=np.multiply(average0,w)
average0_sum=sum(average0_count)
#正常人平均水平
average1_count=np.multiply(average1,w)
average1_sum=sum(average1_count)
#对感情不明确的进行情感分析
train_0 = pd.read_csv('C:/Users/Administrator/Desktop/dasanxia/Thursday9 10 11/drugsComTrain_raw.csv')
test_0 = pd.read_csv('C:/Users/Administrator/Desktop/dasanxia/Thursday9 10 11/drugsComTest_raw.csv')
#仅读入打分为2~9的评论
train_0=train_0[train_0.rating.isin([2,3,4,5,6,7,8,9])]
test_0=test_0[test_0.rating.isin([2,3,4,5,6,7,8,9])]
#去除双引号
train_0.review = train_0.review.apply(remove_enclosing_quotes)
test_0.review = test_0.review.apply(remove_enclosing_quotes)
#去除特殊字符
train_0.review = train_0.review.apply(lambda x: re.sub(r'&#\d+;',r'', x))
test_0.review = test_0.review.apply(lambda x: re.sub(r'&#\d+;',r'', x))
#将症状、药物与评论三者融为一段文字
train_0['text'] = combine_text_columns(train_0, text_cols)
test_0['text'] = combine_text_columns(test_0, text_cols)
#数据归一化
X_0 = vec_alphanumeric.fit_transform(train.text)
#调用模型进行预测
pred_0 = clf_lr.predict(X_0)
#输出预测结果
print(pred_0)
(3) User interface and interface visualization code
This part is the main interface GUI design and sub-interface calling.
#导入库函数
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import QCoreApplication
#GUI界面大小，按键设置
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(1600, 1000)
Dialog.setFixedSize(1600, 1000)
self.label = QtWidgets.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(680, 60, 250, 61))
self.label.setObjectName("label")
self.label.setStyleSheet('font-size:40px')
self.pushButton = QtWidgets.QPushButton(Dialog)
self.pushButton.setGeometry(QtCore.QRect(720, 300, 150, 50))
self.pushButton.setObjectName("pushButton")
self.pushButton.setStyleSheet('font-size:30px')
self.pushButton_2 = QtWidgets.QPushButton(Dialog)
self.pushButton_2.setGeometry(QtCore.QRect(720, 500, 150, 50))
self.pushButton_2.setObjectName("pushButton_2")
self.pushButton_2.setStyleSheet('font-size:30px')
self.pushButton_3 = QtWidgets.QPushButton(Dialog)
self.pushButton_3.setGeometry(QtCore.QRect(720, 800, 150, 50))
self.pushButton_3.setObjectName("pushButton_3")
self.pushButton_3.setStyleSheet('font-size:30px')
self.pushButton_3.clicked.connect(QCoreApplication.instance().quit)
self.retranslateUi(Dialog)
QtCore.QMetaObject.connectSlotsByName(Dialog)
#为按键起名字
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "智能健康助手"))
self.label.setText(_translate("Dialog", "智能健康助手"))
self.pushButton.setText(_translate("Dialog", "健康预测"))
self.pushButton_2.setText(_translate("Dialog", "药物推荐"))
self.pushButton_3.setText(_translate("Dialog", "退出程序"))
Call the machine learning model to analyze the user's data. The relevant code is as follows:
#调用机器学习模型，对用户的数据进行分析
#导入库函数
import csv
import pandas as pd
import pickle
import numpy as np
from PyQt5 import QtCore, QtGui, QtWidgets
from databank import result
#GUI界面大小，按键设置
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(1600, 1000)
Dialog.setFixedSize(1600,1000)
self.label = QtWidgets.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(650,40,300,80)) #标签位置及大小
self.label.setTextFormat(QtCore.Qt.AutoText)
self.label.setAlignment(QtCore.Qt.AlignCenter)
self.label.setObjectName("label")
self.label.setStyleSheet('font-size:40px')
self.age = QtWidgets.QLabel(Dialog)
self.age.setGeometry(QtCore.QRect(700, 200, 200, 40))
self.age.setObjectName("age")
self.age.setStyleSheet('font-size:30px')
self.ageinput = QtWidgets.QLineEdit(Dialog)
self.ageinput.setGeometry(QtCore.QRect(850, 200, 50, 40))
self.ageinput.setObjectName("ageinput")
agelimit = QtCore.QRegExp("[1-9][0-9]{1,2}")
age_validator = QtGui.QRegExpValidator(agelimit, self.ageinput)
self.ageinput.setValidator(age_validator)
font = QtGui.QFont()
font.setPointSize(20)
self.ageinput.setFont(font)
self.sex = QtWidgets.QLabel(Dialog)
self.sex.setGeometry(QtCore.QRect(700, 300, 200, 40))
self.sex.setObjectName("sex")
self.sex.setStyleSheet('font-size:30px')
self.sexinput = QtWidgets.QComboBox(Dialog)
self.sexinput.setGeometry(QtCore.QRect(850, 300, 60, 45))
self.sexinput.setObjectName("sexinput")
self.sexinput.addItem("")
self.sexinput.addItem("")
self.sexinput.setStyleSheet('font-size:30px')
self.taste = QtWidgets.QLabel(Dialog)
self.taste.setGeometry(QtCore.QRect(700, 400, 200, 40))
self.taste.setObjectName("taste")
self.taste.setStyleSheet('font-size:30px')
self.tasteinput = QtWidgets.QComboBox(Dialog)
self.tasteinput.setGeometry(QtCore.QRect(850, 400, 100, 45))
self.tasteinput.setObjectName("tasteinput")
self.tasteinput.addItem("")
self.tasteinput.addItem("")
self.tasteinput.setStyleSheet('font-size:30px')
self.pushButton = QtWidgets.QPushButton(Dialog)
self.pushButton.setGeometry(QtCore.QRect(750, 800, 100, 40))
self.pushButton.setObjectName("pushButton")
self.pushButton.setStyleSheet('font-size:30px')
self.pushButton.clicked.connect(self.do)
self.Button = QtWidgets.QPushButton(Dialog)
self.Button.setGeometry(QtCore.QRect(700, 700, 200, 40))
self.Button.setObjectName("Button")
self.Button.setStyleSheet('font-size:30px')
self.Button.clicked.connect(self.get)
self.Button2 = QtWidgets.QPushButton(Dialog)
self.Button2.setGeometry(QtCore.QRect(750, 950, 100, 40))
self.Button2.setObjectName("Button")
self.Button2.setStyleSheet('font-size:30px')
self.label_2 = QtWidgets.QLabel(Dialog)
self.label_2.setGeometry(QtCore.QRect(900, 700, 200, 40))
self.label_2.setStyleSheet('font-size:30px')
self.label_2.setObjectName("label_2")
self.progressBar = QtWidgets.QProgressBar(Dialog)
self.progressBar.setGeometry(QtCore.QRect(760, 900, 118, 23))
self.progressBar.setProperty("value", 0)
self.progressBar.setObjectName("progressBar")
self.retranslateUi(Dialog)
QtCore.QMetaObject.connectSlotsByName(Dialog)
#读入用户输入数据
def get(self):
age = self.ageinput.text()
sex = self.sexinput.currentIndex()
taste = self.tasteinput.currentIndex()
a = [age,sex,145,233,1,0,150,0,2.3,0,0,0,0,1,0,1,0,0,1,0,0]
b=[age,80,1.02,1,0,0.439252336,0,0,0,121,36,1.2,133.9017857,4.878443114,15.4,1,1,0,taste,0,0]
#将用户数据输出到csv文件，并进行定性化分析
with open('./databank/test.csv', "a",newline='') as file:
csv_file = csv.writer(file)
csv_file.writerow(a)
file.close()
with open('./databank/test2.csv', "a",newline='') as file:
csv_file = csv.writer(file)
csv_file.writerow(b)
file.close()
self.label_2.setText(QtCore.QCoreApplication.translate("Dialog", "写入成功"))
self.progressBar.setProperty("value", 0)
#对用户数据定量化分析
def caculate1(self,client1,client_result1,):
#判断病情，数据来源于模型
average1_sum = 2.94673976 #病人
average0_sum = 3.12316913 #正常人
#权重矩阵
w = np.ones((21, 1))
w[0, 0] = 0.0066 #client_x['age']
w[1, 0] = 0.059 #client_x['ca']
w[2, 0] = 0.0033 #client_x['chol']
w[3, 0] = 0.0590 #client_x['cp_0']
w[4, 0] = 0 #client_x['cp_1']
w[5, 0] = 0.0197 #client_x['cp_2']
w[6, 0] = 0.0131 #client_x['cp_3']
w[7, 0] = -0.0033 #client_x['exang']
w[8, 0] = -0.0033 #client_x['fbs']
w[9, 0] = 0.0098 #client_x['oldpeak']
w[10, 0] = 0.0131 #client_x['restecg']
w[11, 0] = 0.0131 #client_x['trestbps']
w[12, 0] = 0.0098 #client_x['sex']
w[13, 0] = 0 #client_x['slope_0']
w[14, 0] = 0.0033 #client_x['slope_1']
w[15, 0] = 0.0066 #client_x['slope_2']
w[16, 0] = 0 #client_x['thal_0']
w[17, 0] = 0 #client_x['thal_1']
w[18, 0] = 0.0361 #client_x['thal_2']
w[19, 0] = 0.0131 #client_x['thal_3']
w[20, 0] = 0 #client_x['thalach']
#用户数据转化为矩阵
client_message = np.ones((21, 1))
client_message[0, 0] = client1['age']
client_message[1, 0] = client1['ca']
client_message[2, 0] = client1['chol']
client_message[3, 0] = client1['cp_0']
client_message[4, 0] = client1['cp_1']
client_message[5, 0] = client1['cp_2']
client_message[6, 0] = client1['cp_3']
client_message[7, 0] = client1['exang']
client_message[8, 0] = client1['fbs']
client_message[9, 0] = client1['oldpeak']
client_message[10, 0] = client1['restecg']
client_message[11, 0] = client1['trestbps']
client_message[12, 0] = client1['sex']
client_message[13, 0] = client1['slope_0']
client_message[14, 0] = client1['slope_1']
client_message[15, 0] = client1['slope_2']
client_message[16, 0] = client1['thal_0']
client_message[17, 0] = client1['thal_1']
client_message[18, 0] = client1['thal_2']
client_message[19, 0] = client1['thal_3']
client_message[20, 0] = client1['thalach']
client_count = np.multiply(client_message, w)
client_sum = sum(client_count)
#判断用户是否为病人
if client_result1 == 1:
client_index = client_sum / average1_sum
return(client_index)
else:
client_index = client_sum / average0_sum
return(client_index)
#在判断用户是否为病人后继续定量化分析
def caculate2(self,client2,client_result2,):
#判断病情，数据来源于模型
average1_sum = 10.06672071 #病人
average0_sum = 12.62982294 #正常人
#权重矩阵
w = np.ones((21, 1))
w[0, 0] = 0.002 #client_x['age']
w[1, 0] = 0.051 #client_x['bp']
w[2, 0] = 0.0545 #client_x['sg']
w[3, 0] = 0.024 #client_x['al']
w[4, 0] = 0 #client_x['su']
w[5, 0] = 0.179 #client_x['rbc']
w[6, 0] = 0 #client_x['pc']
w[7, 0] = 0 #client_x['pcc']
w[8, 0] = 0 #client_x['ba']
w[9, 0] = 0.028 #client_x['bgr']
w[10, 0] = 0.019 #client_x['bu']
w[11, 0] = 0 #client_x['sc']
w[12, 0] = 0.0025 #client_x['sod']
w[13, 0] = 0 #client_x['pot']
w[14, 0] = 0.1505 #client_x['hemo']
w[15, 0] = 0.08 #client_x['htn']
w[16, 0] = 0.025 #client_x['dm']
w[17, 0] = 0 #client_x['cad']
w[18, 0] = -0.0005 #client_x['appet']
w[19, 0] = 0 #client_x['pe']
w[20, 0] = -0.0005 #client_x['ane']
client_message = np.ones((21, 1))
client_message[0, 0] = client2['age']
client_message[1, 0] = client2['bp']
client_message[2, 0] = client2['sg']
client_message[3, 0] = client2['al']
client_message[4, 0] = client2['su']
client_message[5, 0] = client2['rbc']
client_message[6, 0] = client2['pc']
client_message[7, 0] = client2['pcc']
client_message[8, 0] = client2['ba']
client_message[9, 0] = client2['bgr']
client_message[10, 0] = client2['bu']
client_message[11, 0] = client2['sc']
client_message[12, 0] = client2['sod']
client_message[13, 0] = client2['pot']
client_message[14, 0] = client2['hemo']
client_message[15, 0] = client2['htn']
client_message[16, 0] = client2['dm']
client_message[17, 0] = client2['cad']
client_message[18, 0] = client2['appet']
client_message[19, 0] = client2['pe']
client_message[20, 0] = client2['ane']
client_count = np.multiply(client_message, w)
client_sum = sum(client_count)
#print(client_sum)调试代码
if client_result2 == 1:
client_index = client_sum / average1_sum
return(client_index)
else:
client_index = client_sum / average0_sum
return(client_index)
#输出模型结果，可视化
def do(self):
self.label_2.setText(QtCore.QCoreApplication.translate("Dialog", ""))
#QtWidgets.QMessageBox.about(None, "Warning", "跳转成功")调试代码
client = pd.read_csv("./databank/test.csv")
#QtWidgets.QMessageBox.about(None, "Warning", "打开csv成功")调试代码
with open("./databank/model.pkl", "rb") as f:
rf = pickle.load(f)
list = []
#QtWidgets.QMessageBox.about(None, "Warning", "打开模型成功")调试代码
print(client.shape[0] - 1)
for i in range(0, client.shape[0] - 1):
list.append(i)
client = client.drop(list)
print(client)
#QtWidgets.QMessageBox.about(None, "Warning", "数据载入成功")调试代码
client_result = rf.predict(client)
#QtWidgets.QMessageBox.about(None, "Warning", "预测成功")调试代码
print('这就是分类预测结果')
print(client_result)
self.progressBar.setProperty("value", 25)
self.score1 = self.caculate1(client,client_result)
print("得分结果：", self.score1)
self.progressBar.setProperty("value", 50)
client2 = pd.read_csv("./databank/test2.csv")
with open("./databank/model_kidney.pkl", "rb") as f:
rf2 = pickle.load(f)
list2 = []
for i in range(0, client2.shape[0] - 1):
list2.append(i)
client2 = client2.drop(list)
client2_result = rf.predict(client2)
print('这就是分类预测结果')
print(client2_result)
self.progressBar.setProperty("value", 75)
self.score2 = self.caculate2(client2, client2_result)
print("得分结果：",self.score2)
self.progressBar.setProperty("value", 100)
self.c_widget = QtWidgets.QWidget()
self.c = result.Ui_Dialog() self.c.setupUi(client_result[0],client2_result[0],self.score1[0],self.score2[0],self.c_widget)
self.c.pushButton.clicked.connect(self.c_widget.close)
self.c_widget.show()
#子界面文字设计
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "健康预测系统"))
self.label.setText(_translate("Dialog", "健康预测系统"))
self.age.setText(_translate("Dialog", "您的年龄："))
self.sex.setText(_translate("Dialog", "您的性别："))
self.sexinput.setItemText(0, _translate("Dialog", "男"))
self.sexinput.setItemText(1, _translate("Dialog", "女"))
self.taste.setText(_translate("Dialog", "最近食欲："))
self.tasteinput.setItemText(0, _translate("Dialog", "good"))
self.tasteinput.setItemText(1, _translate("Dialog", "pure"))
self.pushButton.setText(_translate("Dialog", "检测"))
self.Button.setText(_translate("Dialog", "一键获取"))
self.Button2.setText(_translate("Dialog", "返回"))
The prediction results are visualized and the user's data is analyzed qualitatively and quantitatively. The relevant codes are as follows:
#预测结果可视化，对用户的数据定性+定量进行化分析
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import QCoreApplication
from PyQt5.QtWidgets import QMessageBox
#疾病预测结果展示子界面GUI设计，大小按键设计
class Ui_Dialog(object):
def setupUi(self, result1,result2,score1,score2,Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(1600, 1000)
Dialog.setFixedSize(1600, 1000)
self.label = QtWidgets.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(150, 240, 300, 40))
self.label.setObjectName("label")
self.label.setStyleSheet('font-size:35px')
self.label_2 = QtWidgets.QLabel(Dialog)
self.label_2.setGeometry(QtCore.QRect(420, 240, 100, 40))
self.label_2.setObjectName("label_2")
self.label_2.setStyleSheet('font-size:35px')
self.label_3 = QtWidgets.QLabel(Dialog)
self.label_3.setGeometry(QtCore.QRect(100, 400, 537, 213))
self.label_3.setObjectName("label_3")
self.label_4 = QtWidgets.QLabel(Dialog)
self.label_4.setGeometry(QtCore.QRect(950, 240, 300, 40))
self.label_4.setObjectName("label_4")
self.label_4.setStyleSheet('font-size:35px')
self.label_5 = QtWidgets.QLabel(Dialog)
self.label_5.setGeometry(QtCore.QRect(1220, 240, 100, 40))
self.label_5.setObjectName("label_5")
self.label_5.setStyleSheet('font-size:35px')
self.label_6 = QtWidgets.QLabel(Dialog)
self.label_6.setGeometry(QtCore.QRect(900, 400, 537, 213))
self.label_6.setObjectName("label_6")
self.pushButton = QtWidgets.QPushButton(Dialog)
self.pushButton.setGeometry(QtCore.QRect(700, 800, 200, 40))
self.pushButton.setObjectName("pushButton")
self.pushButton.setStyleSheet('font-size:30px')
#需要用到的图片调用地址
self.png1 = QtGui.QPixmap('./databank/healthy1.png')
self.png2 = QtGui.QPixmap('./databank/healthy2.png')
self.png3 = QtGui.QPixmap('./databank/weak1.png')
self.png4 = QtGui.QPixmap('./databank/weak2.png')
self.result1 = result1
self.result2 = result2
self.score1 = score1
self.score2 = score2
self.retranslateUi(Dialog)
QtCore.QMetaObject.connectSlotsByName(Dialog)
#对疾病预测结果进行分析，是轻度或重度中毒
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "预测结果"))
self.label.setText(_translate("Dialog", "您心脏的状态是："))
self.label_4.setText(_translate("Dialog", "您肾脏的状态是："))
self.pushButton.setText(_translate("Dialog", "退出"))
print(self.score1,self.score2)
if self.result1 == 0:
self.label_2.setText(_translate("Dialog", "健康"))
if self.score1 < 1:
self.label_3.setPixmap(self.png1)
#print函数仅在编译时输出，用于检测程序debug
print("ok")
else :
self.label_3.setPixmap(self.png2)
print("ok")
if self.result2 == 0:
self.label_5.setText(_translate("Dialog", "健康"))
if self.score2 < 1:
self.label_6.setPixmap(self.png1)
print("ok")
else :
self.label_6.setPixmap(self.png2)
print("ok")
if self.result1 == 1:
self.label_2.setText(_translate("Dialog", "虚弱"))
if self.score1 < 1:
self.label_3.setPixmap(self.png3)
print("ok")
else :
self.label_3.setPixmap(self.png4)
print("ok")
if self.result2 == 1:
self.label_5.setText(_translate("Dialog", "虚弱"))
if self.score2 < 1:
self.label_6.setPixmap(self.png3)
print("ok")
else :
self.label_6.setPixmap(self.png4)
print("ok")
For drug query, the relevant codes are as follows:
#药物查询
#导入库函数
from PyQt5 import QtCore, QtGui, QtWidgets
from databank import medicineres
#GUI界面大小，按键设置
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(1600, 1000)
Dialog.setFixedSize(1600, 1000)
self.label = QtWidgets.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(680, 60, 250, 61))
self.label.setObjectName("label")
self.label.setStyleSheet('font-size:40px')
self.label_2 = QtWidgets.QLabel(Dialog)
self.label_2.setGeometry(QtCore.QRect(640, 200, 450, 400))
self.label_2.setObjectName("label_2")
self.label_2.setStyleSheet('font-size:25px')
self.pushButton_3 = QtWidgets.QPushButton(Dialog)
self.pushButton_3.setGeometry(QtCore.QRect(720, 800, 150, 50))
self.pushButton_3.setObjectName("pushButton_3")
self.pushButton_3.setStyleSheet('font-size:30px')
self.pushButton_3.clicked.connect(self.getresult)
self.textEdit = QtWidgets.QTextEdit(Dialog)
self.textEdit.setGeometry(QtCore.QRect(660, 500, 291, 201))
self.textEdit.setObjectName("textEdit")
self.textEdit.setStyleSheet('font-size:30px')
self.pushButton_2 = QtWidgets.QPushButton(Dialog)
self.pushButton_2.setGeometry(QtCore.QRect(720, 900, 150, 50))
self.pushButton_2.setObjectName("pushButton_3")
self.pushButton_2.setStyleSheet('font-size:30px')
self.retranslateUi(Dialog)
QtCore.QMetaObject.connectSlotsByName(Dialog)
#接收用户输入，调用数据库
def getresult(self):
condition = self.textEdit.toPlainText()
self.a_widget = QtWidgets.QWidget()
self.a = medicineres.Ui_Dialog()
self.a.setupUi(self.a_widget,condition)
self.a.pushButton.clicked.connect(self.a_widget.close)
self.a_widget.show()
#输出匹配到数据库中的数据
def retranslateUi(self, Dialog):
_translate = QtCore.QCoreApplication.translate
Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
self.label.setText(_translate("Dialog", "药物推荐助手"))
self.label_2.setText(_translate("Dialog","请输入您的症状以#作为间隔"))
self.pushButton_3.setText(_translate("Dialog", "查询"))
self.pushButton_2.setText(_translate("Dialog", "返回"))
Visualize the matched database results. The relevant code is as follows:
#将匹配到的数据库结果可视化
#导入库函数
from PyQt5 import QtCore, QtGui, QtWidgets
import pandas as pd
#GUI界面设计，大小和按钮
class Ui_Dialog(object):
def setupUi(self, Dialog,condition):
_translate = QtCore.QCoreApplication.translate
Dialog.setObjectName("Dialog")
Dialog.setWindowTitle(_translate("Dialog", "查询结果"))
Dialog.resize(600, 400)
Dialog.setFixedSize(600, 400)
self.pushButton = QtWidgets.QPushButton(Dialog)
self.pushButton.setGeometry(QtCore.QRect(200, 300, 200, 40))
self.pushButton.setObjectName("pushButton")
self.pushButton.setStyleSheet('font-size:30px')
self.pushButton.setText(_translate("Dialog", "退出"))
self.tableWidget = QtWidgets.QTableWidget(Dialog)
self.tableWidget.setGeometry(QtCore.QRect(0, 0, 600, 300))
self.tableWidget.setObjectName("tableWidget") self.tableWidget.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers)
self.tableWidget.setColumnCount(3)
item = QtWidgets.QTableWidgetItem()
#接收输入的用户症状
item.setText(_translate("Dialog", "medicine_1"))
self.tableWidget.setHorizontalHeaderItem(0, item)
item = QtWidgets.QTableWidgetItem()
item.setText(_translate("Dialog", "medicine_2"))
self.tableWidget.setHorizontalHeaderItem(1, item)
item = QtWidgets.QTableWidgetItem()
item.setText(_translate("Dialog", "medicine_3"))
print(item)
self.tableWidget.setHorizontalHeaderItem(2, item)
self.condition = condition.split("#")
#读数据库
df = pd.read_csv("./databank/cure_clean.csv")
self.tableWidget.setRowCount(len(self.condition))
for i in self.condition :
medicine = []
medicine_1 = df.loc[df['condition'] == i, 'medicine_1']
medicine_2 = df.loc[df['condition'] == i, 'medicine_2']
medicine_3 = df.loc[df['condition'] == i, 'medicine_3']
if len(medicine_1) == 0 or len(medicine_2) == 0 or len(medicine_3) == 0 :
info = " %s Not Found" % (i)
QtWidgets.QMessageBox.about(None,"Warning",info) self.tableWidget.setVerticalHeaderItem(self.condition.index(i), QtWidgets.QTableWidgetItem(i))
else : self.tableWidget.setVerticalHeaderItem(self.condition.index(i), QtWidgets.QTableWidgetItem(i))
#输出排名前三的药物
medicine_1 = medicine_1.values[0]
medicine_2 = medicine_2.values[0]
medicine_3 = medicine_3.values[0]
medicine_1= medicine_1.strip().replace('Series([], )', ' ')
medicine_2= medicine_2.strip().replace('Series([], )', ' ')
medicine_3= medicine_3.strip().replace('Series([], )', ' ')
medicine.append(medicine_1)
medicine.append(medicine_2)
medicine.append(medicine_3)
for j in medicine:
self.tableWidget.setItem(self.condition.index(i), medicine.index(j),
QtWidgets.QTableWidgetItem(j))
QtWidgets.QTableWidget.resizeColumnsToContents(self.tableWidget)
QtCore.QMetaObject.connectSlotsByName(Dialog)
The relevant code for the test file is as follows:
#测试文件代码
#导入库函数
import PyQt5
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import QCoreApplication
import sys,xlsxwriter,csv,os
from databank import jiance,mainwindow,medicine,medicineres
#打开疾病预测模块，预测疾病；创建或打开疾病记录，记录用户数据
with open("./databank/test.csv", 'w') as f:
csv_write = csv.writer(f)
data_row = ["age", "sex","trestbps","chol","fbs","restecg","thalach","exang","oldpeak","ca","cp_0","cp_1","cp_2","cp_3","thal_0","thal_1","thal_2","thal_3","slope_0","slope_1","slope_2"]
csv_write.writerow(data_row)
f.close()
with open("./databank/test2.csv", 'w') as f:
csv_write = csv.writer(f)
data_row = ["age", "bp","sg","al","su","rbc","pc","pcc","ba","bgr","bu","sc","sod","pot","hemo","htn","dm","cad","appet","pe","ane"]
csv_write.writerow(data_row)
f.close()
#打开药物推荐模块，推荐药物
app = QtWidgets.QApplication(sys.argv)
a_widget = QtWidgets.QWidget()
b_widget = QtWidgets.QWidget()
c_widget = QtWidgets.QWidget()
#界面GUI设计及输出
a = mainwindow.Ui_Dialog()
a.setupUi(a_widget)
a_widget.show()
b = jiance.Ui_Dialog()
b.setupUi(b_widget)
c = medicine.Ui_Dialog()
c.setupUi(c_widget)
a.pushButton.clicked.connect(b_widget.show)
c.pushButton_2.clicked.connect(c_widget.close)
b.Button2.clicked.connect(b_widget.close)
a.pushButton_2.clicked.connect(c_widget.show)
sys.exit(app.exec_())
System test
This section includes training accuracy, testing effects, and model application.
1. Training accuracy
The accuracy of prediction of heart disease reaches more than 89%, and the model training is relatively successful, as shown in Figures 13 to 16.
The prediction accuracy of chronic kidney disease reaches 100%, as shown in Figures 17 to 20.
2. Test effect
Substitute the data into the model for testing, and display and compare the classified labels with the original data to verify that the model can achieve disease prediction and drug recommendation.
The model training effect is shown in the figure.
The effect of drug recommendation is shown in the figure.
3. Model application
Open the cmd command, go to the folder where the program is located; enter to python test.pystart testing; open the application, the initial interface is as shown in the figure.
The interface has three buttons from top to bottom. Click the first button “健康预测”and you can see the interface jump to the disease prediction interface, as shown in the figure.
After returning to the main interface, click the second button “药物推荐”and see the interface jump to the drug recommendation interface, as shown in the figure;
The drug recommendation assistant is shown in Figure 21. For symptoms that are not in the database, it prompts that they do not exist and does not output any disease information, as shown in Figure 22.
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