A data balancing approach designing of an expert system for Heart Disease Prediction

Heart disease is a major global health concern that results in millions of deaths annually. Prevention and effective treatment of heart-related problems depend heavily on early detection and accurate prediction. It was previously predicted accurately with machine learning methods. This innovative development in healthcare has the power to transform preventative care and save a great deal of lives. The study starts with a thorough assessment of the literature that covers a wide range of topics, including pre-processing techniques, performance evaluation measures, datasets used in heart disease research, predictive modeling strategies, diagnostic methodologies, and current issues in the field. Building on these fundamental understandings, the background section describes the particular actions conducted in this investigation, such as the description of the dataset, data pre-treatment techniques, label encoding, feature selection methodology, algorithm selection tactics, and stringent performance evaluation techniques.The results indicate that ensemble methods, particularly random forests, outperformed individual classifiers in predicting heart disease. Key predictors identified included hypertension, cholesterol levels, smoking status, and physical inactivity. The Decision Tree and Random Forest model achieved an accuracy of 99.83%. This work demonstrates how machine learning models, particularly ensemble approaches, can increase the precision of heart disease prediction. In comparison to conventional techniques, the models offer a more reliable risk assessment since they integrate a wide range of variables and sophisticated algorithms. The results open the door to tailored healthcare treatments that facilitate early identification and treatment of cardiac disease.