Which classification algorithm performs better for diabetes dataset?

They found for better accuracy, Adaboost can be applied to predict diseases like diabetes, coronary heart disease, and hypertension. Sisodia et al. [18] found that, among the applied machine learning methods SVM, NB, and DT on PIDD, the NB classifier shows better accuracy at 76.30%.

DiabetesPedigreeFunction: Diabetes pedigree function (a function which scores likelihood of diabetes based on family history ) Age: Age (years) Outcome: Class variable (0 if non-diabetic, 1 if diabetic)

Recently, numerous algorithms are used to predict diabetes , including the traditional machine learning method (Kavakiotis et al., 2017), such as support vector machine (SVM), decision tree (DT), logistic regression and so on.