Abstract

The research presented in this article focuses mostly on different data mining techniques that are used in the field of cardiovascular disease. Prediction. The human heart is the most important organ in the body. Body. Essentially, it is responsible for regulating blood flow throughout our bodies. Any abnormality in the heart's rhythm may induce discomfort in other areas of the body. Any kind of disruption to the regular functioning of the body is considered. Heart illness is a condition that affects the heart. In today's world, heart disease is one of the most common ailments in today's society. The most common causes of death are listed below. Heart disease is a possibility. Develop as a result of a poor lifestyle, smoking, drinking, and being overweight Consumption of fats that may increase the risk of hypertension. At the University of California, Irvine machine learning repository the planned work is described below. forecasts the likelihood of heart disease and assigns a risk classification to the patient various data mining methods, such as regression analysis and classification Naive Bayes, Decision Trees, Logistic Regression, and Random Forests are all examples of statistical models. Forest. As a result, this article provides comparative research conducted by evaluating the effectiveness of various machine learning techniques algorithms. The findings of the study confirm that Random Forest is effective

Description