153abf0819177105026-7878 20240826015801674 beie:beie support@latticescipub.com WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 08 30 2024 13 9 Department of Computer Science and Engineering, AP-IIIT, RGUKT, RK Valley, Idupulapaya, Kadapa, India. Ratna Kumari Challa https://orcid.org/0000-0001-5077-8513 Buduri Reddaiah https://orcid.org/0000-0002-5851-2194 Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India. Kanusu Srinivasa Rao https://orcid.org/0000-0002-9850-3110 Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India. Krishnaiah Pulluru Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India. Ranga Swamy Sirisati1 Department of Computer Science & Engineering, Vignan’s Institute of Management and Technology for Women, Kondapur, Ghatkesar. Venkata Narayana Reddy Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India. Diabetes is one of the most lethal diseases in the world. It is also a precursor to various other disorders such as coronary failure, blindness, and kidney diseases. Patients often need to visit diagnostic centers to get their reports after consultation, which requires a significant investment of time and money. However, with the growth of machine learning methods, we now have the ability to address this issue. Advanced systems utilizing information processing can forecast whether a patient has diabetes or not. Furthermore, early prediction of the disease can provide patients with critical interventions before it fully develops. Data mining techniques can extract hidden information from large datasets of diabetes-related information. The aim of this research is to develop a system that can predict the diabetic risk level of a patient with higher accuracy. The model development is based on classification methods such as K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM) algorithms. For K-Nearest Neighbors, the models achieve an accuracy of 71%, 78% for SVM, and 70% for the Decision Tree algorithm. The outcomes demonstrate a significant accuracy of these methods. 08 30 2024 22 27 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE): https://www.ijitee.org/ Based on my understanding, this article has no conflicts of interest. This article has not been sponsored or funded by any organization or agency. The independence of this research is a crucial factor in affirming its impartiality, as it has been conducted without any external sway. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijitee.I9952.13090824 https://www.ijitee.org/portfolio-item/I995213090824/