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1. Problem analysis
Based on the personal history, disease history, onset-related information, and the results of the first imaging examination of the first 100 patients, our task is to build a prediction model to predict the mRS score (degree of recovery) of the remaining 60 patients in the next 90 days. ). This model will help the medical team better understand the patient's recovery prospects and take appropriate treatment and rehabilitation measures.
First, data related to personal history, disease history, onset-related information, and first imaging results need to be collected. This includes the patient's basic information (such as age, gender), disease history (such as history of hypertension, stroke, diabetes, etc.), disease-related information, and relevant characteristics of the first imaging examination in the provided data set.
For the collected data, we need to perform data cleaning and preprocessing, including handling missing values, outliers and data type conversion. Categorical variables also need to be coded for use in modeling.
Given the large number of features that may be included in the data, feature selection is required to determine which features are most important for the prediction of 90-day mRS scores. Feature selection can be performed using statistical methods, feature importance analysis, correlation analysis and other methods.
Depending on the nature of the problem, different types of predictive models can be chosen. This could be a regression model, regressing the seven values of the 90-day mRS score. question