## 2 Literature Review
### Machine Learning in Heart Attack Prediction  
One major area where ML algorithms have seen extensive application is the prediction of heart attack after acute myocardial infarction (MI), which involves analyzing clinical information for patterns and correlations [32]. Decision Tree (DT) and Logistic Regression (LR) are two examples of popular, easy-to-understand algorithms. Many new models, including neural networks, SVM, and RF, have entered the field of heart failure (HF) prognosis and prediction; these models enhance predictive performance by capitalizing on intricate nonlinear interactions between features [2,32].  
[PERSON] et al. [33] applied ML algorithms, including RF, Regression, KNN, and NB, on electronic health records and clinical reports to forecast heart attacks. The findings indicate that the most suitable ML algorithm for early cardiovascular disease detection is RF, achieving an accuracy of 88.52%. [PERSON] et al. [32] investigated the potential of ML algorithms to enhance clinical decision-making and personalize patient treatment. ML algorithms were used to analyze patient data from Portuguese hospitals to forecast acute MI (AMI) mortality. Stochastic Gradient Descent achieved an area under the curve (AUC) value of 79%, which was increased to 81% for SVM, after adding laboratory variables. Moreover, the model was improved using FE and SMOTE with an AUC of 88% and a recall of 80%. [PERSON] and [PERSON] [34] presented a DL approach to automated MI detection utilizing ultrashort-term heart rate variability (HRV) analysis of single-lead ECGs. The proposed system extracted time and frequency domain, and nonlinear HRV features to predict the occurrence of MI using an ANN model. The model achieved a sensitivity level of 100% and an accuracy of 99.1%.  
[PERSON] et al. [35] assessed ML models with the goal of forecasting in-hospital mortality after AMI. The observation was that for the risk classifications of high-risk people, a marginal increase in performance when compared to LR was seen while using the XGBoost classifier model (C statistic 0.90 vs. 0.89). It indicates that while ML models did not significantly enhance overall discrimination, XGBoost surpassed other models in calibration and risk resolution, indicating that XGBoost has the potential to enhance AMI risk assessment. Furthermore, [PERSON] et al. (2019) similarly examined the efficacy of several ML models in predicting HF occurrences after AMI and proposed the HF-Lab9 model. Among the seven ML algorithms evaluated, XGBoost outperformed the remaining with key features, including glucose levels, lipids, and troponin I. An AUC of 0.966 was achieved by the XGBoost-based HF-Lab model, demonstrating both predictive accuracy and clinical value in the early evaluation of HF risk for patients with AMI.  
[PERSON] (2017) used ML to forecast the likelihood of AMI or mortality in patients presenting with acute chest pain by analyzing their ECG and blood tests performed at the time of presentation. Input characteristics such as age, sex, ECG, and first blood tests were used to create two ML models based on LR and ANN, utilizing data from 9,519 individuals. A convolutional neural network (CNN) performed better than traditional approaches by ruling out 55% of instances and ruling in 5.3% of instances without requiring further tests.