Integrating Machine Learning for Accurate Prediction of Early Diabetes: A Novel Approach

Kailash Chandra Bandhu, Ratnesh Litoriya, Aditi Rathore, Alefiya Safdari, Aditi Watt, Swati Vaidya and Mubeen Ahmed Khan
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Kailash Chandra Bandhu: Medi-Caps University, India
Ratnesh Litoriya: Medi-Caps University, India
Aditi Rathore: Medi-Caps University, India
Alefiya Safdari: Medi-Caps University, India
Aditi Watt: Medi-Caps University, India
Swati Vaidya: Medi-Caps University, India
Mubeen Ahmed Khan: Medi-Caps University, India

International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 2023, vol. 13, issue 1, 1-24

Abstract: In the current world, where diabetes is day by day becoming a very common and fatal disease, it's important that proper measures be taken in order to deal with it. As per the studies, early prediction of diabetes can lead to improved treatment to avoid further complications of the disease, and in order to do so efficiently, machine learning techniques are a great deal. In this study, various factors are taken into consideration, like blood pressure, pregnancy, glucose level, age, insulin, skin thickness, and diabetes pedigree function, which together can be useful to predict whether a person has a risk of developing diabetes or not and help society with the early diagnosis of diabetes. This model is trained using three main classification algorithms, namely support vector, random forest, and decision tree classifiers. The prediction results of each of the classifiers are summarized in this study, and the decision tree gives 78.89% accuracy.

Date: 2023
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International Journal of Cyber Behavior, Psychology and Learning (IJCBPL) is currently edited by Nadia Mansour Bouzaida

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