Pattern Recognition & Predictive Analysis of Cardiovascular Diseases: A Machine Learning Approach

Abstract

Cardiovascular disease (CVD) is a predominant global health concern, with its impact becoming increasingly pronounced in low- and middle- income countries due to challenges like limited healthcare access, inadequate public awareness, and lifestyle-related risks. Addressing CVD's multifactorial origins, which span genetic, environmental, and behavioral domains, requires advanced diagnostic techniques. This research leverages the UCI Heart Disease dataset to develop a deep learning predictive model for CVD, incorporating 14 vital heart health parameters. The models performance is critically assessed against conventional machine learning approaches, shedding light on its efficiency and areas of refinement. Utilizing sophisticated Neural Network structures, this study strives to enhance predictive health analytics, aiming for timely CVD identification and intervention. As the integration of machine learning into healthcare deepens, it's crucial to ensure that these tools are robust, thoroughly evaluated, and augment clinical insights to reduce misdiagnosis risks.