Mellitus Preliminary Analysis using Various Data Mining Algorithms and Metrics

s-Diabetes mellitus, also called diabetes, is a metabolic disease that causes increased blood sugar. In diabetic patients, insulin is taken, as this hormone transfers sugar into the cells from the blood. At an early stage, prediction of diabetes can lead to better treatment. It is possible to predict diabetes using data mining, deep learning, machine learning, etc. Data mining is widely used for prediction, prognosis, analysis, risk factors. The proposed research work is primarily focused on the prediction of diabetes in patients in this paper, based on highest accuracy. It is possible to use classification methods based on diabetic data to predict the outcome or to discover whether or not the patient is affected. Throughout this document, a predictive model has been developed by utilizing five data mining classification model, like Naive Bayes, SMO, Multiclass, Random Forest, IBK, to predict early stage diabetes and calculate accuracy. All five algorithms are measured on different metrics, i.e, Accuracy, Recall, Precision.