DDPIS: Diabetes Disease Prediction by Improvising SVM

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Author(s): Shivani Sharma (ABES Institute of Technology, India), Bipin Kumar Rai (ABES Institute of Technology, India), Mahak Gupta (ABES Institute of Technology, India)and Muskan Dinkar (ABES Institute of Technology, India)
Copyright: 2023
Volume: 12
Issue: 2
Pages: 11
Source title: International Journal of Reliable and Quality E-Healthcare (IJRQEH)
Editor(s)-in-Chief: Anastasius Moumtzoglou (Hellenic Society for Quality & Safety in Healthcare and P. & A. Kyriakou Children's Hospital, Greece)
DOI: 10.4018/IJRQEH.318090

Keywords: Health Information Systems / Medical Information Science Reference / Medicine & Healthcare

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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.

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DDPIS: Diabetes Disease Prediction by Improvising SVM

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