Utilizing Machine Learning Algorithms For Predicting Cardiovascular Disease

Abstract

Cardiovascular disease (CVD) remains a leading cause of global mortality. Early prediction and intervention are vital for improving outcomes and reducing healthcare costs. This study leverages a comprehensive Kaggle dataset, including clinical, demographic, and lifestyle information, to enhance predictive models for CVD. By employing machine learning algorithms like Logistic Regression and Random Forest, the research aims to construct accurate and interpretable models. The study ensures methodological robustness and reproducibility by utilizing tools such as Google Colab and Microsoft Excel for data manipulation and analysis. The performance of these supervised learning algorithms is compared to identify the most accurate model for predicting CVD. Improved prediction accuracy can facilitate early identification and intervention, potentially lowering CVD incidence and improving patient outcomes. The study’s findings could significantly impact healthcare by enabling more targeted and effective preventive measures against CVD.