Study and Implementation of Classifiers for the Risk of Diabetes Prediction
Keywords:
Decision Tree, Artificial Neural Network, Logistic Regression, Naive Bayes, Random ForestAbstract
The framework enables the client to make an utilization of calculations
to anticipate the danger of diabetes mellitus in the human body. The
different grouping models, for example, Decision Tree, Artificial Neural
Networks, Logistic Regression, Association rules, and Naive Bayes are
utilized in this framework. At that point the Random Forest system is
utilized to discover the exactness of each model in the task. The dataset
utilized is the Pima Indians Diabetes Data Set, which has the data of
patients, some of them have creating diabetes, along these lines, this
venture is planned to make a portable application for anticipating an
individual’s class whether present in of the diabetes hazard or not.
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