CARDIAC DISEASE PREDICTION USING RANDOM FOREST WITH LINEAR MODEL

Making forecasts and diagnosing ailments has never been simple for medical professionals when it comes to heart conditions. Due to this, people can take the necessary action to treat heart disease before it gets worse if it is discovered in its early stages anywhere in the world. The three main causes of heart disease-drinking alcohol, smoking cigarettes, and not exercising-have become serious issues in recent years. The health care industry has produced a substantial amount of data over time, which has made machine learning capable of providing effective outcomes in prediction and decision-making. Only male patients risk level for heart disease is predicted by the Heart Disease Prediction (HDP) in the current system, which is created using the Naive Bayes and Decision Tree algorithms. For prediction, the algorithm makes use of medical parameters such age, sex, smoking status, BMI, and physical health, etc. A patients likelihood of developing heart disease is predicted by the HDP. Fishers Discriminant Ratio is one of the methods for feature selection based on random forest (RF), which is recommended to identify the best features for heart disease prediction and enhance the precision of RF-based classification. Our goal in this study is to identify the best factors that can improve the prediction accuracy of heart disease and finding the most effective variables to raise the accuracy of heart disease prediction. Evaluation criteria, namely accuracy, specificity, sensitivity, and area under the ROC curve, are employed to verify the efficacy of the proposed approach on a public dataset comprising patients of both genders. The primary benefits of applying machine learning for heart disease prediction are that it reduces the complexity of the doctors time, is patient- and cost-friendly, and manages the largest (enormous) amount of data through feature selection and the random forest algorithm. Early diagnosis of cardiovascular disease can help with lifestyle modifications for high-risk patients, which can lower complications and be a significant medical milestone. KEYWORDS: Random Forest, Decision Tree, Naive Bayes, Feature Selection, and Evaluation Metrics.