Heart disease prediction using machine learning

Abstract

Heart disease is getting increasingly widespread, and it has a high fatality rate throughout the world. Heart disease has become a major health concern for many individuals and the leading cause of mortality worldwide in the previous decade. This is a challenging process that must be completed accurately and effectively. The study report focuses on which people are more prone to acquire heart disease depending on a variety of medical factors. We created a heart disease prediction method based on the patient's medical history that predicts whether the patient is likely to be diagnosed with a heart disease or not. The research title is "Heart Disease Prediction Using Machine Learning" and it focuses on the prediction of heart disease as well as showing who is impacted by heart disease and who is not based on the patient's medical data. Machine learning may provide an effective decision-making solution as well as precise forecasts. In the medical field, machine learning techniques are commonly used. Researchers favor models based on supervised learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), and ensemble models.