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Cardiovascular disorders represent a general and critical health concern worldwide, which needs an innovative approaches to increase prediction and prevention strategies. This research paper introduces a cutting-edge predictive model for cardiovascular disorders, named Predictive model for cardiovascular diseases, designed to leverage the advancements in machine learning and data analytics. The model integrates a diverse array of health parameters, including physiological metrics, genetic markers, lifestyle factors, and medical history, to create a comprehensive and dynamic risk assessment framework. Cardiovascular diseases pose a significant threat to human health, emphasizing the urgent necessity for early diagnosis to reduce associated mortality rates. This research proposes an advanced predictive model that incorporates diverse methods to achieve effective heart disease prediction. To ensure the success of our model, we employ efficient data collection, pre-processing, and transformation techniques to generate accurate training information.
Shenoi et al. (Thu,) studied this question.