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@article{158947,
        author = {Neha Titarmare and Srushti Jamnerkar and Shreya Khewale and Shiba Malkhede and Anushka Meshram and Pranjali Urkude},
        title = {DIABETES PREDICTION USING AN ASSEMBLAGE OF ML CLASSIFIERS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {1149-1153},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158947},
        abstract = {Many different machine learning techniques are used in a range of disciplines to do predictive analytics over massive data. Despite the challenges, predictive analytics in healthcare can ultimately help practitioners make quick decisions regarding the health and treatment of patients based on enormous amounts of data. Six different machine learning algorithms are used in this research effort, which addresses predictive analytics in healthcare. A collection of patient medical records is acquired for experimental purposes, and six different machine-learning algorithms are applied to the dataset. It is discussed and contrasted how well the used algorithms perform and how accurate they are. The algorithm that is most effective for diabetes prediction can be determined by comparing the various machine learning methods employed in this study. Through the use of machine learning techniques, this initiative seeks to aid medical professionals in the early diagnosis and treatment of diabetes.},
        keywords = {diabetes, machine learning algorithms, healthcare},
        month = {},
        }