Abstract
Cardiovascular disease (CVD) remains the leading cause of death globally. In search of advanced techniques for early detection of CVD, recent research has increasingly focused on using machine learning (ML) methods to improve the accuracy and timeliness of diagnosis. A multifactorial machine learning approach offers a comprehensive solution for cardiovascular disease detection, using vast and diverse datasets to develop predictive models that outperform traditional methods. This paper provides a comprehensive examination of various machine learning approaches and their application in the early detection of cardiovascular abnormalities, with special emphasis on their effectiveness compared to traditional diagnostic methods. The research methodology involves the implementation of several ML models trained and tested using large datasets that provide analysis covering various demographic parameters, lifestyle parameters and health status parameters. Key findings show that ML models significantly outperform traditional statistical methods in detecting early signs of CVD. The superior performance of ML models represents a promising tool for healthcare professionals, potentially leading to better strategies for preventive care and reduction of CVD-related mortality. The ongoing development and refinement of these technologies, along with improvements in data collection and interoperability between healthcare systems, will be critical to realizing their full potential in the clinical setting.