Heart Disease Prediction Using Machine Learning Techniques

For the purpose of making wise decisions, the health care sectors gather enormous amounts of data that may contain some hidden information. Some sophisticated data mining techniques are employed for producing acceptable findings and making sensible judgement based on data. In this paper, a Heart Disease Prediction System (HDPS) is created to predict the risk level of heart disease utilizing the Logistic regression and Decision Tree algorithms. The algorithm makes predictions using 15 medical characteristics, including age, sex, blood pressure, cholesterol, and obesity. The HDPS forecasts the probability that people may develop heart disease. It makes possible important knowledge. For instance, it is necessary to identify relationships between patterns and medical factors associated to heart disease. The training approach we used was a by different algorithms in machine learning like Random Forest, KNN, Support Vector Machine, Logistic Regression Decision Tree. The outcomes show that the developed diagnostic system can accurately identify the risk level of cardiac illnesses.