Machine Learning Models for Automated Heart Disease Prognosis

As reported by the Centers for Disease Control and Prevention (CDC), heart disease is the major cause of death for both sexes and members of most racial and ethnic groups. Cardiovascular disease claims one life in the United States every 34 seconds. The expense of medical services, medications, and lost productivity because of deaths from heart disease causes the United States to lose a significant amount of money each year. At least half of all Americans have one or more of the following three major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other significant indicators are the presence of diabetes, obesity (high body mass index), a lack of physical activity, and excessive alcohol consumption. In recent times, the healthcare sector has discovered that machine learning is a practical and reliable decision-making tool. The latter tends to assist doctors in making informed decisions about the heart condition of a patient. It has become increasingly important to develop an effective early-stage cardiac disease prognosis using digital patient information to enhance clinical decision-making. This study intends to find the most accurate machine learning classifiers for these prognostic uses. To predict cardiac disease, several supervised machine-learning algorithms were used and compared in terms of accuracy and performance. The following methods were used in our research: Logistic Regression, Random Forest, Decision Tree, and K Nearest Neighbor. The heart disease dataset from Kaggle has been used to validate this research. The latter was created using data from the 2020 United States national health status survey of more than 410,000 people, performed yearly by the centers for disease control and prevention.