Cardiovascular Health Prognosis: Machine Learning Approaches for Precise Heart Disease Prediction

Cardiovascular diseases, including heart diseases, represent a prevalent global health concern. The prevalence of coronary heart diseases in Indian regions varies, lesser in rural areas and more in urban areas. Numerous factors contribute to the risk of developing cardiovascular diseases. Leveraging Machine Learning (ML) algorithms to discern patterns within data for predictive purposes is a burgeoning area of research. In this study, we propose a methodology to ascertain the likelihood of an individual developing heart disease using ML algorithms. Initially, datasets are employed to reduce the dimensionality of features. Subsequently, various ML algorithms are applied to the processed data. Among the models assessed, the Logistic Regression Classifier demonstrated the highest accuracy. The results obtained, including accuracy (88.16%), precision (90%), recall (81.82%), and f-measure (85.71%), signify promising outcomes for predicting heart disease development.