Prediction of Heart Disease Using Machine Learning Techniques

Abstract

A potential strategy in the healthcare industry is the prediction of cardiac disease using machine learning algorithms. Worldwide, heart disease continues to be one of the major causes of death, and successful treatment and prevention depend greatly on early identification. Large volumes of patient data may be analyzed using machine learning algorithms to find patterns and risk factors that might lead to the onset of heart disease. These algorithms use supervised learning, unsupervised learning, and ensemble approaches to assess a variety of data sources, including clinical test results, patient demographics, and medical records. Machine-learning algorithms may be trained on historical data from a variety of patients to discover complicated associations and generate precise predictions about a person's risk of acquiring heart disease. Our objective is to create a machine-learning technique that reliably predicts heart disease and is computationally effective. Feature selection is a crucial step in the creation of prediction models as it permits the identification of the most significant risk factors for heart disease. Machine learning methods including logistic regression, support vector machines, decision trees, random forests, and neural networks are often used to predict cardiac disease. By examining extensive patient data, machine learning algorithms show considerable potential in the prediction of cardiac disease. In the battle against heart disease, their capacity to spot patterns and risk factors may result in early identification, individualized therapies, and better patient care.