## 2 Related Works
They considered ten features, age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, and oldpeak, for predicting cardiovascular disease. Redundant features were eliminated using [PERSON]'s correlation coefficient method. In this work, the ML models were trained using 80.20 hold-out validation, and they achieved an accuracy of 87.85% with the CatBoost algorithm. Further, the CatBoost algorithm has been trained using a 10-fold CV and achieved an accuracy ranging between 88.67% to 98.11%, with a mean accuracy of 94.34%. [PERSON] et al. (2018) developed a machine learning model to identify the risky people suffering from heart disease by splitting the Cleveland heart dataset into a 70:30 ratio for training and testing. The model performed standardization of data to the standard scales and trained various algorithms. It attained the highest accuracy of 92.30% using Logistic Regression. Furthermore, they tuned the KNN classifier with a k value between 2 and 20 and achieved the best accuracy of 90.11% at k = 14. [PERSON] et al. (2018) performed standardization of heart dataset attributes for best prediction results, followed by hyperparameter tuning of machine learning classifiers using the GridSearchCV method to improve the accuracy of heart disease prediction. They evaluated the performance of the classifier models and concluded that the standardization and hyperparameter tuning improved the prediction accuracy. An accuracy of 96.72%, a precision of 98%, a recall of 98%, and an F-measure of 98% were achieved using an SVM classifier with a Sigmoid kernel and complexity parameter, C = 0.5. A similar methodology block diagram proposed by this work is given in Figure 1.  
From Figure 1, the work in (2018) has not employed the feature selection to avoid noise or redundant data. [PERSON] et al. (2018) developed a python-based application for heart disease detection using the Random Forest algorithm by performing explorative data analysis of heart disease dataset features. The importance of heart dataset attributes using a correlation matrix plot was evaluated, and an accuracy of 83% was achieved in training data. [PERSON] et al. (2013) assessed the predictive ability of machine learning algorithms in CVD. They developed a comprehensive strategy with MEDLINE, Embase, and Scopus databases to predict coronary artery disease (CAD), stroke, heart failure, and cardiac arrhythmias. They achieved an AUC of 0.88, and 0.93 with boosting and custom-built algorithms, respectively, to predict CAD. They obtained an AUC of 0.92, 0.91, 0.90 with SVM, boosting, and CNN algorithms, respectively, to predict stroke. They were not able to provide a confusion matrix for the meta-analytic approach to report all possible evaluation matrices. [PERSON] et al. (2014) developed a machine learning model based on AutoPrognosis to improve heart disease risk prediction with non-traditional variables using UK Biobank (473 variables). They compared the model with Framingham score, and Cox proportional hazards (CoxPH) models in terms of AUC-ROC. They obtained an AUC-ROC of 0.774 compared to that of Framingham score and Cox PH models is 0.724, and 0.734, respectively. The main drawback is that the cholesterol biomarkers were not included in the development of a model. [PERSON] et al. (2015) discussed feature selection methods that provide a ranking for the attributes of the dataset to select the best among them in heart disease risk prediction. Several data mining techniques, namely random trees (RTs), decision tree (C5.0), Chi-squared automatic interaction detection (CHAID), and SVM, were utilized to obtain the best-ranked attributes of the Z-Alizadeh Sani heart dataset comprising 303 observations with 55 attributes. An accuracy of 91.47% and AUC of 96.7% have been achieved by selecting 40 attributes with the random tree method. [PERSON] et al. (2016) proposed an ensemble of deep learning neural networks to classify heart disease patients from the dataset of 800 samples with 141 features collected from Medica Norte Hospital.