Investigating Machine Learning Models in Acute Myocardial Infarction to Predict Mortality

Abstract

Objective: Pakistan and the rest of the world suffer from a high rate of acute myocardial infarctions (AMIs). In this study, we employed a machine learning model to predict mortality in patients with Acute Myocardial Infarction (AMI). By analyzing various variables, they assessed the impact of these factors on the predictive models, highlighting the potential of machine learning in improving mortality prediction and informing clinical decision-making in AMI cases.

Methodology: This study conducted three experiments using a Kaggle dataset to predict mortality in Acute Myocardial Infarction (AMI) patients with machine learning. Relevant input features were selected, and three models (SVM, DT, KNN) classified mortality status. Model performance was rigorously evaluated with metrics like Accuracy, AUC, Precision, Recall, and F1-score. Data preprocessing, including handling missing values and normalization, preceded model training.

Results: Among the evaluated models, the Support Vector Machine (SVM) exhibited the highest accuracy of approximately 87.66% and demonstrated robust discrimination capabilities, with an AUC score of 0.796. Precision, recall, and F1 scores indicated a balanced trade-off between correctly identifying negative outcomes and effectively capturing positive cases.

Conclusion: The SVM model emerged as the most promising classifier, showcasing strong potential for predicting patient mortality in the context of AMI. However, further refinements and optimizations may be necessary to enhance model performance, ensuring its clinical relevance and utility in real-world medical scenarios.