Abstract: Diabetes mellitus, a prevalent global health issue, demands early detection and effective management. For deeper analysis and diabetes prediction, this study uses ML methodologies. A large dataset including clinical, sociological, and biological features is meticulously processed. A wide range of ML methods are used to initiate predictive models. This study enhances the science of diabetes prediction by giving effective tools for early risk assessment, personalized medications, and optimal healthcare management. These breakthroughs have the potential to improve public health outcomes and help combat the diabetes epidemic.
Keywords: Diabetes, prediction, analysis, e-Health, data processing, machine learning
Cite:
Dr. P.V.R.D. Prasada Rao, Asritha Musunuru, Subhash Alapati, Abhinava Kamireddy, Venkatesh Jajula,"Diabetes Analysis using Machine Learning with KNN", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13327.
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DOI:
10.17148/IJARCCE.2024.13327
