A Machine Learning Model for the Prediction of Heart Attack Risk in High-Risk Patients Utilizing Real-World Data

Abstract

Heart disease is a significant global public health concern that impacts a vast number of individuals worldwide. The early identification of patients at risk of heart attack can significantly reduce mortality rates. In this research study, we employed machine learning methods to develop a model that predicts the likelihood of a heart attack. To create the model, we collected a real-world dataset of patient features, including demographic information, medical history, and lifestyle factors. We pre-processed the data to eliminate any missing values and standardized the features to ensure uniformity across the dataset. Additionally, we utilized feature engineering techniques to identify the most significant factors that contribute to the development of heart attacks. We evaluated several machine learning algorithms such as logistic regression, decision trees, and random forest to identify the most effective ones based on traditional metrics including accuracy, precision, recall, F1-score, Mathew correlation, ROC, and AUC. Our algorithm produced highly accurate predictions for heart attack risk. Our results demonstrate that machine learning algorithms can effectively predict heart attacks and identify high-risk patients. The model can be integrated into electronic health records to facilitate prompt identification and intervention by healthcare providers. However, our study has limitations that need to be addressed, including the requirement for validation on a larger and more diverse dataset as well as the challenge of interpreting the model. Future research may incorporate additional data sources, advanced machine-learning techniques, and improved model interpretability. Our heart attack prediction model holds significant potential as a valuable tool for healthcare practitioners to identify high-risk patients and decrease heart attack rates.