Abstract
Diabetes, the fastest-growing life-threatening disease, presents significant global health challenges, with 3.8 million annual deaths and affecting 422 million individuals worldwide. This study employs RapidMiner, a popular data mining tool, to analyze a public diabetes dataset. Addressing common dataset challenges such as extraneous features, efficient feature selection, and algorithm implementation, the research introduces a novel approach utilizing the Naive Bayes algorithm. Despite inherent complexities, the Naive Bayes model achieves a commendable accuracy rate of 75.51%, showcasing its applicability in diabetic data analysis within the RapidMiner environment. This research sheds light on the potential of integrating RapidMiner and data mining techniques to advance our understanding and management of diabetes-related data, thereby contributing to improved healthcare outcomes and patient well-being.
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