• Ankush Gadge
• Monika Biradar
• Vishal Awate
• Nisha Bankar
• Prof. S. V. Shinde

Diabetes is a highly prevalent condition that affects people all around the globe. Long-term effects of diabetes include heart disease and renal failure, among other things. If this condition is discovered early, people may live longer and enjoy better lives. Nowadays, it seems that the healthcare business generates a large amount of data, which may be analyzed using proper machine learning algorithms and tools to deliver better insights. This kind of analysis aids in the early detection of rare and difficult-to-diagnose disorders, resulting in a higher cure success rate and lower medical costs. The goal of this study is to develop a model that will be able to predict the likelihood of diabetes in patients with the maximum accuracy. Three machine learning methods that may be utilized for data classification were selected for this research, and they are as follows: Multilayer Perceptron and Support Vector Machine On the diabetic data set, all Machine Learning Techniques were used to identify diabetes and detect stages of diabetes, and their results were analyzed using different metrics. In this study, an early-stage diabetes risk prediction dataset from the UCI machine learning repository was used. In a huge dataset, this research successfully demonstrated the capacity to detect individuals with early diabetes risks. Multilayer perceptron seems to categories the patient as diabetic or not with a better degree of accuracy than the other classification algorithms.

"SMART E-HEALTH SYSTEM: Machine learning based e-health system for diseases prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.b707-b712, June-2022, Available :http://www.jetir.org/papers/JETIR2206193.pdf

"SMART E-HEALTH SYSTEM: Machine learning based e-health system for diseases prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppb707-b712, June-2022, Available at : http://www.jetir.org/papers/JETIR2206193.pdf