Heart attack prediction using machine learning

According to the World Health Organization (WHO) 2020 report, more than 17.9 million people lose their lives due to heart attacks each year, making it one of the deadliest diseases worldwide; furthermore, heart attack is classified as a cardiovascular disease that affects the lives of many people globally. Machine learning, vital to the healthcare industry, enhances people's quality of life along with scientific advancements. In this thesis, studies on predicting heart attacks using machine learning algorithms were conducted. The data used in the studies were obtained from the UCI Machine Learning database. The prediction of heart attacks was carried out using five well-known machine learning models mentioned in the literature. Additionally, a new conceptual model integrating data preprocessing, hyperparameter tuning, and Logistic Regression was proposed for predicting heart attacks in this thesis. Tests conducted on the proposed model yielded satisfactory results, achieving an accuracy rate of 93%. Furthermore, the results produced by the proposed model were compared with the outcomes of other machine learning algorithms, demonstrating that the proposed model outperforms other algorithms.