6.1 Performance Measures

\label{performance-measures}
Accuracy : Accuracy of a model is defined as the total positive instances of the model are divided by the total number of instances. Accuracy parameter provides the percentage of correctly classified instances. The accuracy of model is defined as
\begin{equation} Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\nonumber \\ \end{equation}
Sensitivity : This parameter is used to determine the degree of the attributes to correctly classify the person with diseases and is defined as
\begin{equation} Sensivity=\frac{\text{TP}}{TP+FN}\nonumber \\ \end{equation}
Specificity : This parameter is used to determine the degree of the attributes to correctly classify the person without diseases and is defined as
\begin{equation} Specificity=\ \frac{\text{TN}}{TN+FP}\nonumber \\ \end{equation}
Area under curve: Area under curve (AUC) is the important parameter which is used to assess the performance of diagnostic tests as well as to identify the prevalence of a disease. It is two dimensional plots between the sensitivity and specificity and measure the validity of medical tests.
Figure 4: Proposed PSO-ANN based diagnostic model