Comparative Study of Machine Learning Based Diabetes Predictive System
Ratna Kumari Challa1, Buduri Reddaiah2, Kanusu Srinivasa Rao3, Krishnaiah Pulluru4, Ranga Swamy Sirisati5, Venkata Narayana Reddy6
1Ratna Kumari Challa, Department of Computer Science and Engineering, AP-IIIT, RGUKT, RK Valley, Idupulapaya, Kadapa (A.P.), India.
2Buduri Reddaiah, Department of Computer Science and Technology, Yogi Vemana University, Kadapa (A.P.), India.
3Kanusu Srinivasa Rao, Department of Computer Science and Technology, Yogi Vemana University, Kadapa (A.P.), India.
4Krishnaiah Pulluru, Department of Computer Science and Technology, Yogi Vemana University, Kadapa (A.P.), India.
5Ranga Swamy Sirisati, Department of Computer Science & Engineering, Vignan’s Institute of Management and Technology for Women, Kondapur (Telangana), Ghatkesar.
6Venkata Narayana Reddy, Department of Computer Science and Technology, Yogi Vemana University, Kadapa (A.P.), India.
Manuscript received on 15 July 2024 | Revised Manuscript received on 20 July 2024 | Manuscript Accepted on 15 August 2024 | Manuscript published on 30 August 2024 | PP: 22-27 | Volume-13 Issue-9, August 2024 | Retrieval Number: 100.1/ijitee.I995213090824 | DOI: 10.35940/ijitee.I9952.13090824
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Diabetes, SVM, KNN, Decision tree, Accuracy
Scope of the Article: Computer Science and Applications