A Different Methodologies to Predict Heart Disease Using Machine Learning Techniques.

Heart disease is the severe human disease, and it has a major impact on people’s health. Heart disease occurs when the heart fails to supply blood to every part of the body. Early detection of heart disease, can help to reduce mortality. Heart disease diagnosis must be accurate and timely in order to prevent and cure heart failure. In many ways, the diagnosis of heart disease based on standard medical history has been regarded unreliable. For differentiating healthy people and people with heart problems, non-invasive strategies like machine learning are accurate and effective. The heart disease dataset can be used to build a machine learning system to predict heart disease. The 7 machine learning algorithms, 7 performance assessment parameters like specificity, sensitivity, Matthews corelation coefficient, accuracy, and cross-validation strategy, 3 feature selection methodology are used to find the people with heart problems. The suggested methodology can quickly distinguish between those with heart disease and those who are healthy. The main objective is to identify heart illness using different methodologies of machine learning.