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Abstract: Diabetes may be a common, chronic illness. Prediction of this disease at AN early stage will result in improved treatment. data processing techniques square measure wide used for prediction of illness at AN early stage. during this analysis paper, polygenic disease is foretold exploitation vital attributes, and also the relationship of the differing attributes is additionally characterised. numerous tools square measure accustomed verify vital attribute choice, and for bunch, prediction, and association rule mining for polygenic disease. vital attributes choice was done via the principal part analysis methodology. Our findings indicate a robust association of polygenic disease with body mass index (BMI) and with aldohexose level, that was extracted via the Apriori methodology. Artificial neural network (ANN), random forest (RF) and K- means bunch techniques were enforced for the prediction of polygenic disease.Moreover, we have a tendency to additionally compared the results achieved exploitation this method and several other standard machine learning algorithms approaches like Support Vector Machine (SVM), call Tree (DT), K-Nearest Neighbor (KNN), Naïve theorem Classifier (NBC), Random Forest Classifier (RFC), provision Regression (LR). machine results of our projected methodology show not solely that abundant fewer options square measure required, however additionally higher prediction accuracy are often achieved (95% for GWO - MLP and ninety seven for APGWO - MLP). This work has the potential to be applicable to clinical follow and become a supporting tool for doctors/physicians.