Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1113 2018 2018 Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Nandini Gupta Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Shubhangi .. Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Hardik .. Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Surinder Kaur Diabetes nowadays is a familiar and long-term disease. If a prediction is made early, better treatment can be provided. The preprocessing data approach is extremely useful in predicting the disease at an early stage. "Many tools are used in determining significant characteristics such as selection, Prediction, and association rule mining for diabetes. The principal component analysis method was used to select significant attributes. Our judgments denote a strong association of diabetes with body mass indicator (BMI) and glucose degree. The study implemented logistic regression, decision trees, and ANN techniques to process Pima Indian diabetes datasets and predict whether people at risk have diabetes. It was analyzed that random forest had the best accuracy of 80.52 %. Out of 500 negative records & 268 positive records, our model correctly analyzed 403 records & 216 records, respectively. 2020 2020 49 65 10.54216/FPA.010201 https://www.americaspg.com/articleinfo/3/show/1113