Analysis of Heart Disease Using the Random Forest Method

Since heart disease is one of the leading causes of death worldwide, lowering the death rate requires early detection and precise analysis. Machine learning (ML) is one method of analysis. This study uses the Random Forest process, which is applied through the Weka program, to analyze heart disease. An ensemble learning process called Random Forest can categorize data on datasets with a lot of variables with a high degree of accuracy. The heart disease dataset used in this study includes patient medical information, including age, gender, blood pressure, cholesterol, and the results of various tests. To determine the variables that significantly impact the risk of heart disease and to determine the degree of model accuracy in making predictions, the data was processed using Weka and tested using the Random Forest algorithm. The study's findings demonstrate that the Random Forest process performs well in assessing heart illness with a high degree of accuracy and can provide information about the variables that affect the evaluation of heart disease. As a result, this approach may be a useful one for assisting with early heart disease identification and treatment decisions.