Prediction of Cholesterol and Assessing Cardiovascular Disease Using CNN

Cholesterol is vital for cellular function and overall health but excessive levels significantly increase the risk of cardiovascular diseases. Traditional cholesterol assessment methods rely on invasive blood tests, which are often expensive, inconvenient, and unsuitable for frequent monitoring. This research introduces a noninvasive cholesterol level prediction model utilizing Convolutional Neural Networks (CNNs). By analyzing socioeconomic, behavioral, and clinical factors—such as age, BMI, dietary habits, physical activity, smoking behavior, and medical history—the model identifies complex correlations to accurately estimate cholesterol levels. Designed for integration into healthcare platforms, the system provides an alternative to conventional methods, offering high precision without laboratory testing. This CNN-based approach aims to enhance early detection and encourage proactive health management by providing real-time cholesterol monitoring. The project methodology involves data collection, preprocessing, model training, and validation to ensure its reliability and scalability. The system demonstrated remarkable accuracy, recall for high-risk cases, and efficiency in generating predictions. By leveraging AI-driven techniques, the study contributes to healthcare innovation, enabling cost-effective, accessible, and personalized cholesterol assessment. This research highlights the transformative potential of machine learning in preventive care and aims to reduce the global burden of cardiovascular diseases through timely interventions IndexTerms: Cholesterol prediction, cardiovascular diseases, Convolutional Neural Networks, AI in healthcare, preventive strategies, non-invasive diagnostics.