aspg-3-1113-1791417624 20261008000024 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2020 1 2 Nandini Gupta Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Shubhangi Malik Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Hardik Chawla Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Surinder Kaur Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA 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 49 65 1113 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.010201 https://www.americaspg.com/journal/3/article/1113 https://www.americaspg.com/storage/5086.pdf