## 2 Related Work
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Limitations** & **Methodology** & **Evaluation Metrics** \\ \hline Feature selection problem & Proposed an FCMIM feature selection & Accuracy, Sensitivity, Precision, MCC \\  & technique [2] & Accuracy, Sensitivity, Precision, \\ \hline Prone to overfitting problem & Proposed an EDCNN technique [17] & Accuracy, Sensitivity, Precision, \\  & Specificity \\ \hline The existing techniques have low & Proposed a five-layered DNN & \\ accuracy for predicting heart disease & architecture named HEARO-5 to improve & Accuracy \\  & model accuracy [3] & \\ \hline The existing techniques have parameter & Proposed a model comprising of LR, RF, & \\ fine tuning problems and they are time & ANN, KNN, and SVM to accurately & \\ consuming & predict heart disease [18] & Precision \\ \hline State-of-the-art ML models cannot handle & Used an ensemble DL technique to & \\ high-dimensional datasets & predict heart disease and feature fusion & Accuracy \\  & to extract important features [19] & \\ \hline Feature scaling problem & ANN, DT, RF, SVM, and KNN [20] & Accuracy \\ \hline Computationally expensive & Proposed a Stacked SVM model [21] & Accuracy, Sensitivity, Specificity, MCC \\ \hline Existing techniques are not ideal for heart & Chi-square statistical test along with & \\ disease risk prediction & SVM, GNB, LR, XGBoost, and RF & Accuracy, ROC \\  & algorithms & \\ \hline ML techniques lead to overfitting & \multirow{2}{*}{KNN, DT, LR, Simple Vector Classifier, and GNB [22]} & \multirow{2}{*}{Accuracy} \\  & for large data samples & \\ \hline Existing techniques have scalability & SVD feature selection technique along & \\ issues & with Decision tree classifier model CART, & Accuracy, Precision, Recall \\  & LR, and GNB [23] & \\ \hline Prior techniques are inefficient for heart & Proposed stack model to beat the other & \\ failure prediction & single models [24] & Accuracy, Precision, Recall, F1-score \\ \hline Underfitting and overfitting issues faced & Proposed a \(X^{2}\)-DNN hybrid model [25] & Accuracy, Sensitivity, Specificity, MCC \\ by existing models & Proposed ML and end-to-end DL models & \\ \hline Heart sounds are not efficiently detected & Proposed ML and end-to-end DL models & \\ by existing techniques & to detect heart sounds [26] & Accuracy, Sensitivity, Specificity \\ \hline Patients having heart failure risk are not & Proposed a sequential model based on & \\ well identified using DL & Bi-LSTM [27] & AUC, Precision, Recall \\ \hline Existing techniques have poor & \multirow{2}{*}{Proposed a traditional LR based model} & \multirow{2}{*}{AUC-ROC} \\ performance in detecting heart failure & for prediction [28] & \\ \hline Existing techniques might not extract & Proposed hybrid model using CNN and & \\ high-level features & ConvLSTM [29] & TPR, FPR, FNR, TNR, Accuracy \\ \hline Hyperparameter tuning problem & Proposed IDMPF using ML, and the Grid & ROC, Precision, test score \\  & search algorithm is used [30] & \\ \hline Current techniques are not suitable for & Proposed CNN for feature extraction and & Accuracy, Precision, Recall, F1-score \\ extracting features & disease prediction [31] & \\ \hline Poor accuracy of prior techniques & Proposed model based on ensemble & \\  & techniques [32] & Accuracy, MCC, F1-score \\ \hline Existing techniques cannot handle big & Proposed two-class decision jungle, & \\ data and are prone to overfitting training & two-class LR, two-class boosted DT, and & \\ data & two-class NN for prediction [33] & Accuracy, Precision, Recall, F1-score \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Related work summary.