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@article{155471,
        author = {DR.R. MURUGANANTHAM and M..SOWMYASREE and A.SAI RUSHIK and G.BHANU KIRAN},
        title = {MODELLING A VOTING-BASED MODEL FOR DIABETES PREDICTION USING LEARNING MODELS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {986-991},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155471},
        abstract = {Diabetes mellitus is defined as a collection of metabolic problems that significantly impact human health worldwide. Wide-ranging study into all aspects of diabetes (diagnostic, pathophysiology, therapy, etc.) has ushered in an era of massive amounts of data. This investigation aims to provide a prediction model using machine learning, data analysis methodologies and tools in diabetic prediction. The primary goal of this work is to design a method that can more accurately predict diabetes in patients. Here, a novel ensemble model is evaluated using several characteristics such as precision, accuracy, F-measure, and recall. The machine-learning techniques are identified after hyper-tuning and cross- validation (CV) and then employed in the Vote-based ensemble model ( ). According to the findings, the proposed framework can get an excellent result of approximately 92% accuracy.},
        keywords = {Diabetes, learning approaches, feature analysis, prediction, accuracy},
        month = {},
        }