## 4 Results
### Calculation of Performance Parameters for ANFIS  
The confusion matrixes for each fold, for the adaptive neuro-fuzzy inference system for renal cancer, are shown in Tables 17-20. In these tables, the number of test cases, which are classified correctly as well as the test cases which are classified into the incorrect class are described.  
From Table 21, the values of true positive, false negative, false positive and true negative can be evaluated as 21, 00, 01 and 28, respectively. Similarly, these values are carried out from Tables 22-24. Table 25 describes the average value of performance parameters calculated for the four folds of k-fold cross validation.  
[PERSON] et al. [27] proposed an inference system for the detection of renal cancer by using the adaptive neuro-fuzzy technique. The dataset in the numeric form is provided to the system for training and testing. The triangular membership functions are used for developing the medical inference system. The accuracy, sensitivity, specificity and precision parameters are calculated for the performance evaluation of the system. The system achieved 96%, 95.5%, 96.1% and 95.8% accuracy, sensitivity, specificity and precision, respectively.  
After comparing the performances of [PERSON] et al. [27] with the proposed system, it is observed that the adaptive neuro-fuzzy inference system developed in this research work is better than the existing system. Hence, the ANFIS is more accurate than FIS. The performance of both developed systems is shown in Table 26.  
The calculated estimation of both proposed systems is also represented by using a bar chart. In the bar chart, the two axes are there. One is the \(X\)-axis which is horizontal, and it will represent the proposed models that are fuzzy and ANFIS. Another axis is the \(Y\)-axis which is vertical and presents the percentages. The bar height views the obtained percentage of performance parameters of models. Figure 15 depicts a bar chart for the comparison of two models.  
From the bar chart, it is evaluated that an intelligent medical diagnostic system for renal cancer using the adaptive neuro-fuzzy inference system is more accurate, and precise as compared to the fuzzy inference system for the detection of patients suffering from renal cancer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models/Parameters** & **Classification Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline Fuzzy & 96\% & 95.44\% & 96.48\% & 95.45\% \\ \hline ANFIS & 98\% & 97.61\% & 98.27\% & 97.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 26: Calculated parameters from the developed systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Yes** & **Class Name** \\ \hline
29 & 00 & No \\ \hline
01 & 20 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 24: Matrix with reduced dimensions for fold 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Overall** \\ \hline \(Sensitivity=\frac{(TP)}{(TP+FN)}\) & 100\% & 95.23\% & 100\% & 95.23\% & 97.61\% \\ \hline \(Specificity=\frac{(TN)}{(TN+FP)}\) & 96.55\% & 96.55\% & 100\% & 100\% & 98.27\% \\ \hline \(Precision=\frac{(TP)}{(TP+FP)}\) & 95.45\% & 95.23\% & 100\% & 100\% & 97.67\% \\ \hline \(Classification\ Accuracy=\frac{(TP+TN)}{(TP+FP+TN+FN)}\) & 98\% & 96\% & 100\% & 98\% & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 25: Calculated performance parameters for ANFIS.