Cardiovascular Disease Prediction Using Machine Learning

Abstract

Cardiovascular diseases (CVDs) remain one of the leading causes of mortality worldwide, highlighting the urgent need for reliable, efficient, and early diagnostic tools. Traditional diagnosis often depends on manual interpretation of clinical test results, which can be time-consuming and prone to human error. In this study, we propose a machine learning (ML)-based framework for predicting cardiovascular disease risk by analyzing patient health records. The framework incorporates multiple supervised learning algorithms, including Logistic Regression, Decision Trees, Random Forests, and Neural Networks, to classify patients into risk categories. Data preprocessing techniques such as normalization, encoding, and feature selection were employed to ensure robustness and model accuracy. The models were trained and validated on publicly available healthcare datasets, and their performance was evaluated using standard metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Results demonstrate that the ML-based approach provides higher diagnostic accuracy compared to conventional methods, enabling early interventions and improved patient outcomes. Furthermore, a user-friendly web-based interface was developed using Streamlit to support real-time predictions, making the system practical for clinical use. This study highlights the potential of ML-driven decision support systems in transforming preventive healthcare and aiding clinicians in the early diagnosis and management of cardiovascular diseases.