DDPIS: Diabetes Disease Prediction by Improvising SVM

Abstract

An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by improvising support vector machine is a platform that predicts diabetes based on the data entered into the system and offers reliable results based on that data. Earlier, the dataset consisted of a smaller number of features comprising the patients' medical details that were useful in determining the patient's health condition and was mainly focused on gestational diabetes, which only deals with pregnant women. In this work, the authors build a system that is more efficient than the previous system because of these reasons. It provides more accurate results by improvising the support vector machine, which includes more datasets and can predict the possibility of diabetes disease in both males and females.