# Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Research Author** & **Method** & **\# Attr.** & **Acc. (\%)** & **Pre.** & **Sen.** & **AUC** \\ \hline [PERSON] et al. [18] & LR with PCA & 7 & 87.0 & - & 0.85 & - \\ \hline [PERSON] et al. [19] & Majority vote with NB, BN, RF, and MP & 9 & 85.48 & - & - & - \\ \hline [PERSON] et al. [20] & LR and GNB with Single value decomposition & 4 & 82.75 & - & - & 0.87 \\ \hline [PERSON] et al. [21] & Random Forest & 10 & 85.71 & - & - & 0.8675 \\ \hline [PERSON] et al. [22] & FAMD + RF & 28 & 93.44 & - & 0.8928 & - \\ \hline [PERSON] et al. [23] & SMO & 14 & 84.0741 & - & - & 0.8392 \\ \hline [PERSON] et al. [25] & SMO & 14 & - & 0.84 & 0.8365 & - \\ \hline [PERSON] et al. [27] & ANN & 14 & 85.86 & - & 0.8394 & - \\ \hline [PERSON] et al. [28] & HRFLC (RF + AdaBoost + Pearson Coefficient) & 11 & 79.0 & 0.78 & 0.79 & - \\ \hline [PERSON] et al. [29] & Correlation-based feature selection with NB & 6 & 84.818 & - & - & 0.905 \\ \hline [PERSON] et al. [29] & Fuzzy Rough Set and Chi-square FS with Radial bias function (RBF) & 7 & 81.188 & - & - & 0.261 \\ \hline [PERSON] et al. [32] & Two-tier ensemble PSO & 7 & 85.6 & - & - & 0.8586 \\ \hline [PERSON] et al. [43] & Forward feature selection with Radial Basis Function SVM & 7 & 81.19 & - & 72.92 & - \\ \hline
**Proposed method** & **Chi-Squared + SMO** & **11** & **86.468** & **0.865** & **0.865** & **0.861** \\ \hline \end{tabular}
\end{table} 
Table 13: Performance comparison of related works. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Attribute set** & **Acc. (k = 1)** & \begin{tabular}{c} **Acc.** \\ **(k’)** \\ \end{tabular} & **Impr. (\%)** & **MAE** & **Sen.** & **Fallout** & **Pre.** & **F-Mea.** & \begin{tabular}{c} **ROC** \\ **Area** \\ \end{tabular} & **Spe.** \\ \hline \begin{tabular}{c} Full \\ attributes \\ \end{tabular} & 76.897 & - & - & 0.184 & 0.769 & 0.235 & 0.769 & 0.769 & 0.764 & 0.790 \\ \hline \begin{tabular}{c} Full \\ attributes \\ \end{tabular} & 76.897 & \begin{tabular}{c} 84.158 \\ (k = 5) \\ \end{tabular} & 7.260 & 0.228 & 0.842 & 0.166 & 0.842 & 0.841 & 0.893 & 0.880 \\ \hline \begin{tabular}{c} CfsSubset \\ \end{tabular} & 78.877 & \begin{tabular}{c} 83.498 \\ (k = 11) \\ \end{tabular} & 4.620 & 0.237 & 0.835 & 0.178 & 0.839 & 0.833 & 0.889 & 0.910 \\ \hline \begin{tabular}{c} Chi-Squared \\ \end{tabular} & 77.887 & \begin{tabular}{c} 86.138 \\ (k = 9) \\ \end{tabular} & 8.250 & 0.224 & 0.861 & 0.146 & 0.862 & 0.861 & 0.905 & 0.900 \\ \hline \
\begin{tabular}{c} Relieff \\ \end{tabular} & 76.897 &
\begin{tabular}{c} 84.488 \\ (k = 9) \\ \end{tabular} & 7.590 & 0.224 & 0.845 & 0.165 & 0.847 & 0.844 & 0.904 & 0.900 \\ \hline \end{tabular}
\end{table} 
Table 12: Performance comparison of the KNN algorithm by tuning the parameter ’k.’This research work utilized the Cleveland heart dataset to achieve the highest accuracy of 85.148% with the SMO model based on the full set of attributes and an accuracy of 84.158% with the NB model based on an optimal set of seven attributes obtained from correlation-based feature selection. The SMO classifier further achieved the best prediction accuracies of 86.468% and 86.138% from the optimal sets obtained from chi-squared (11 attributes) and ReliefF (10 attributes) techniques, respectively. The best values of other performance metrics, namely, MAE (0.135), sensitivity (0.865), specificity (0.90), fallout (0.142), precision (0.865), and F-measure (0.864), was obtained from SMO with the chi-squared method. The bagging + LR classifier provided an ROC area of 0.91 on both the full attributes and optimal sets obtained from the ReliefF method. Nevertheless, the ensemble classifiers AdaBoost and bagging fell short in their predictions compared to the SMO, while the bagging + REPTree classifier achieved the highest improvement in accuracy of about 3% with the ReliefF method. Tuning of the hyperparameter 'k' in IBk reached an improvement in accuracy of 8.25% with the chi-squared evaluator for k = 9.