# Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
## 4 Results
As shown in Table 10, the highest accuracy of 86.138% was achieved with the sequential minimal optimization (SMO) algorithm, followed by the bagging with LR algorithm, with the highest accuracy of 85.148% based on the optimum attribute set, which was obtained using the ReliefF attribute evaluation method. Furthermore, the SMO algorithm produced the best MAE of 0.138, sensitivity of 0.861, fallout of 0.145, precision of 0.862,  
Figure 8: ROC curves of the optimal set obtained using chi-squared attribute evaluation: (**a**) naïve Bayes classifier and (**b**) logistic regression classifier. A comparison of the accuracy values using the full set of attributes of the Cleveland dataset and optimal attribute sets obtained using various attribute selection techniques performed in this work is shown in Table 11. The correlation-based feature selection method was not able to provide an accuracy value greater than that of the full attribute space. However, there was an improvement in accuracy of about 2% and 1% from the IBk and AdaBoostM1 + DS classifiers, respectively. Besides providing the highest accuracy of 86.468% from SMO, the chi-squared attribute evaluation technique improved most of the classifiers' performances, except for the NB, LR, and AdaBoostM1 + LR classifiers. An increase in the accuracy of about 2% was attained using the JRip algorithm and 1% using the IBk and Bagging + LR algorithms. The ReliefF attribute evaluation method offered the highest improvement in accuracy of about 3% when using the bagging + REPTree classifier, followed by 1.65% from the IBk classifier and about 1% from the SMO and AdaBoostM1 + DS classifiers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & **Full** & \multirow{2}{*}{**CfsSubset**} & **Diff.** & **Chi-Squared** & **Diff.** & **ReliefF** & **Dif.** \\ \hline NB & 83.828 & 84.158 & 0.330 & 83.498 & \(-\)0.330 & 84.488 & \(-\)0.660 \\ \hline LR & 84.818 & 83.168 & \(-\)1.650 & 84.488 & \(-\)0.330 & 84.488 & \(-\)0.330 \\ \hline SMO & 85.148 & 83.828 & \(-\)1.320 & 86.468 & 1.320 & 86.138 & 0.990 \\ \hline IBk/KNN & 76.897 & 78.877 & 1.980 & 77.887 & 0.990 & 78.547 & 1.650 \\ \hline AdaBoostM1 + DS & 82.838 & 83.828 & 0.990 & 83.498 & 0.660 & 83.828 & 0.990 \\ \hline AdaBoostM1 + LR & 84.818 & 83.168 & \(-\)1.650 & 84.488 & \(-\)0.330 & 84.488 & \(-\)0.330 \\ \hline Bagging + REPTree & 80.858 & 81.188 & 0.330 & 82.178 & 1.320 & 83.828 & 2.970 \\ \hline Bagging + LR & 84.488 & 83.828 & \(-\)0.660 & 85.478 & 0.990 & 85.148 & 0.660 \\ \hline JRip & 74.917 & 74.257 & \(-\)0.660 & 76.897 & 1.980 & 74.587 & \(-\)0.330 \\ \hline RF & 81.848 & 79.538 & \(-\)2.310 & 83.168 & 1.320 & 81.188 & \(-\)0.660 \\ \hline \multicolumn{7}{c}{Diff—difference.} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy comparison of the attribute selection techniques. Figure 10: Performance metrics based on the optimal set obtained using the Relieff attribute evaluation. Besides the training of the ML classifier on the full and optimal attribute sets obtained from the attribute evaluators, the hyperparameter 'number of nearest neighbors k' tuning was performed for various values of k = 3, 5, 7, 9, 11, 13, 15, 17, 19, and 21 in the IBk classifier. The best accuracy, accuracy improvement, and other performance metrics for specific 'k' values that were attained from the parameter tuning are presented in Table 12. Though the accuracy delivered by the IBk classifier was slightly less than that of the SMO classifier that was obtained from the chi-squared attribute set, i.e., 86.468%, there was a significant improvement in accuracy by tuning the hyperparameter 'k' value in all the cases. We observed that the greatest accuracy improvement of about 8.25% came from the chi-squared attribute evaluation with k = 9 compared to that of default parameter k = 1. The performance comparison of this research work with the related works is presented in Table 13.