Confusion matrix - the sensitivity and specificity of understanding

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Confusion matrix - understand the sensitivity and specificity

definition

$Sensitivity Sensitivity = \ frac {TP} {TP + FN}$
$Specificity Specificity = \ frac {TN} {TN + FP}$

Case

Assuming that a total of 100,000 patients, among them suffering from cancer of 200 people, or 0.2 percent prevalence rate, the actual test results are as follows:

	A positive test	Test negative	total
The actual positive	160	40	200
The actual negative	29940	69860	99800
total	30100	69900	1000000

Based on the above data:
$Sensitivity Sensitivity = \ frac {TP} {TP + FN} = \ frac {160} {160 + 400} = 80.0 \%$
$Specificity Specificity = \ frac {TN} {TN + FP} = \ frac {69860} {69860 + 29940} = 70.0 \%$

Namely:
sensitivity (also known as the recall) represents the actual number of cases detected in the proportion of the total number of cases reached 80%, the proportion of patients that can detect real out
specifically indicates that the test without sick people, determining exclude 70% probability of illness, will have 30% chance of being tested positive, but not actually sick. That can not be detected proportion of real illness.

Better model, so that all samples can be distributed in four quadrants proportion more optimized to enhance the sensitivity and specificity, increasing the proportion of the sample positive diagonal, i.e. improved detection.

ROC curve