## 2 Related Work  
In [2], the authors proposed a model that accurately predict heart disease using various ML techniques, such as Logistic Regression (LR), ANN, Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Decision Tree (DT). The most important features are selected using different feature learning techniques. The simulation results indicated that the proposed feature selection algorithm Fast Conditional Mutual Information (FCMIM) is compatible with the SVM classifier and achieved 85% classification accuracy. [PERSON] et al. [17] introduced an Enhanced-DL-assisted Convolutional NN (ED-CNN) to help doctors predict CVD using Internet of Things (IoT). The EDCNN model contains an MLP model. The validation is performed via full features and feature reduction. With reduced number of features, the accuracy of the model is recorded as well as the processing time. The proposed model is compared with other DL models such as ANN, DNN, Ensemble DL-based Smart Healthcare System (EDL-SHS), Recurrent NN (RNN), and NN Ensemble (NNE). The test results show that the proposed model achieved a 99.1% precision score. In [3], the authors introduced DNN for predicting heart disease. Different techniques are used, such as cross validation and Matthews Correlation Coefficient (MCC), to evaluate architectures. The dataset on which this survey is performed is publicly available. The proposed model scored 99% accuracy and outperformed the base models. [PERSON], [PERSON], and [PERSON] introduced various ML techniques in [18], such as LR, Random Forest (RF), ANN, SVM, and KNN, to diagnose heart disease using the Cleveland Clinic dataset. They also performed a comparison among these techniques. They found that after applying ML techniques, SVM gave the best result, with an accuracy of about 90%. A Smart Healthcare Monitoring System (SHMS) was proposed in [19] for predicting heart disease. The authors used ensemble DL and Feature Fusion (FF) approaches. FF was used to generate healthcare data by combining features from electronic medical records and sensor data. Features that were irrelevant and redundant were eliminated using the information gain technique, and important features were selected. The authors of this paper also compared their proposed system with other state-of-the-art models. The proposed system achieved an accuracy of 98.5%. In [20], the authors introduced DL algorithms, such as ANN and SVM to predict heart disease. The authors used the Cleveland dataset for training. The final results showed that ANN and SVM models performed best in terms of detecting chronic heart disease. In [21], the authors proposed a new model based on two SVM models to predict heart disease at early stages. The first SVM was used to remove the redundant features and the second SVM was used for prediction. For optimization, the authors applied the Hybrid Grid Search Algorithm (HGSA). After training and testing, the model achieved a 3.3% higher accuracy than the standard SVM model. [PERSON] et al. proposed different ML models such as LR, Gaussian Naive Bayes (GNB), SVM, RF, and eXtreme Gradient Boosting for understanding and reducing heart disease symptoms. Chi-square feature extraction technique is used to select specific features. It was found that RF obtained an accuracy of 88.5% during validation. [PERSON] et al. [22] addressed the problem faced by medical sciences to detect CVD. The authors proposed multiple comparisons to record different predictions. The simulation results showed that the DT classifier outperformed other ML models and achieved an accuracy of 98%. In [23], the authors applied various, data exploratory techniques to extract hidden patterns. Different ML algorithms were used to predict heart disease and seek better performance in prediction. The techniques that were used are GNB, DT, and LR. It was found that LR and GNB achieved the same accuracy of 82.75%. However, for the Area under Receiver Operating Characteristic (AUC-ROC) curve, GNB's value was higher than LR.