• Harasis Singh
• Richa Sharma

Diabetes increases the danger of heart condition by about fourfold in women but only around twice in men. After a heart attack, the outcomes are severe in women too. Women also are at higher risk of other diabetes-related complications like blindness, renal disorder, and depression. The modeling of support vector machines may also be a promising classification method for identifying people within the population with common diseases such as diabetes and pre-diabetes.We illustrate different SVM techniques to detect women with diabetes based on the sample of the women population. For the classification of patients with diabetes and without diabetes based on the set of diabetes-related variables, Compared to other kernels used for SVM, the RBF kernel SVM algorithm can predict the chances of diabetes with 83 percent accuracy. To demonstrate a user-friendly and platform-independent application that allows for person or community assessment with a configurable, user-defined threshold, the Docker-based web tool for Diabetes Classifier was developed. Using common variables, this method can be further explored and updated for other complex diseases.

Diabetes Prediction , SVM Classification Algorithm , Machine Learning

"Diabetes Prediction Using Different Kernels of SVM Classification Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.290-295, March-2021, Available :http://www.jetir.org/papers/JETIR2103045.pdf

"Diabetes Prediction Using Different Kernels of SVM Classification Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp290-295, March-2021, Available at : http://www.jetir.org/papers/JETIR2103045.pdf