Abstract
Abstract: The increasing prevalence of chronic illnesses, including diabetes-related conditions and heart disease, poses a challenge to international healthcare systems. Reducing the detrimental effects of chronic disorders on patient outcomes requires early detection and treatment. This study investigates the possible uses of deep learning and predictive analysis in illness forecasting, with an emphasis on diabetes and heart disease specifically. Strict preparation methods were used, making use of a substantial dataset from the reference dataset source, to guarantee data quality. List-specific models and architectures were used for training and validation in order to assess how well different deep learning models performed in the prediction of sickness. The results show the potential of the suggested technique in the early diagnosis of sickness. They contain notable findings and promising performance metrics. By providing insight into the feasibility and efficacy of deep learning models for the prognosis of diabetes and heart-related illnesses, this work contributes to the expanding corpus of knowledge in healthcare analytics.
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