CARDIOVASCULAR DISEASE PREDICTION USING AI AND ML

Abstract: Cardiovascular diseases are among the leading causes of global mortality, making early prediction and preventive diagnosis a critical requirement in modern healthcare systems. This paper presents an artificial intelligence and machine learning–based approach for predicting cardiovascular disease using patient clinical and lifestyle data. The proposed system applies supervised machine learning algorithms to analyze key medical attributes such as age, blood pressure, cholesterol levels, blood glucose, heart rate, and behavioral factors. Effective data preprocessing techniques, including data cleaning, normalization, and feature selection, are employed to enhance model accuracy and reliability. The trained model identifies complex patterns and relationships within the dataset to classify individuals based on cardiovascular risk levels. Experimental results demonstrate that the proposed approach achieves improved prediction performance compared to traditional diagnostic methods. The system provides a scalable, cost-effective, and decision-supportive solution that can assist healthcare professionals in early detection and risk assessment of cardiovascular diseases.

Keywords: Cardiovascular Disease Prediction, Artificial Intelligence, Machine Learning, Supervised Learning, Medical Data Analysis, Risk Assessment, Healthcare Analytics.