An Intelligent Machine Learning Framework for Early Prediction of Heart Disease

Cardiovascular diseases rank among the major causes of deaths in various parts of the world and therefore early detection is critical in terms of early diagnoses and treatment. This paper offers a smart machine learning model to predict heart disease early in clinical and demographic data. There are data preprocessing, exploratory data analysis, feature engineering, and running the machine learning models, which are Decision Tree, Random Forest, and Logistic Regression. An intelligent hybrid model is created to enhance prediction performance by use of a voting classifier which is a combination of Logistic Regression and an optimized random forest model. Hyperparameter optimization is implemented to improve the efficacy of models and feature importance analysis is implemented to single out crucial risk factors. The models will be measured by accuracy, precision, recall, and F1-score. It has been found that the hybrid model is more accurate and reliable than individual models. The suggested system can help healthcare providers in the early diagnosis and improved decision-making, which will contribute to a decrease in the risk of severe heart-related conditions.