Application of Data Mining methods in diabetes prediction - 2018


Knowledge science methods have the potential to profit other scientific fields by shedding new lightweight on common questions. One such task is facilitate to create predictions on medical knowledge. Diabetes mellitus or merely diabetes is a disease caused thanks to the increase level of blood glucose. Numerous traditional methods, primarily based on physical and chemical tests, are obtainable for diagnosing diabetes. The methods strongly primarily based on the information mining techniques can be effectively applied for high blood pressure risk prediction. During this Project, we explore the first prediction of diabetes via five totally different information mining ways as well as: GMM, SVM, Logistic regression, ELM, ANN. The experiment result proves that ANN (Artificial Neural Network) provides the highest accuracy than alternative techniques.

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