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