Predictive Machine Learning Model for Detection and Classification of Diabetes

T. Parvin¹ , T. Nasrin² , J. Khatun³ , M. Chatterjee⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.9 , pp.11-17, Sep-2021

Online published on Sep 30, 2021

Copyright © T. Parvin, T. Nasrin, J. Khatun, M. Chatterjee . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Many of the intriguing and relevant uses of machine learning may be seen in a medical organization. The concept of machine learning is quickly gaining attention in the healthcare industry. The research and analysis on the medical datasets facilitate people to take proper precautions and care so that diseases can be prevented. Different methodologies are extensively employed in the development of disease prediction decision support systems using a set of medical datasets. This paper aims to explore the different types of machine learning algorithms that can help in the prediction and decision-making of diseases. In this paper, we focus on the early prediction of diabetes. Diabetes is one of the world`s fastest-growing diseases, and it necessitates constant monitoring. To analyze this, we consider several machine learning techniques that can aid in the early detection of this disease.

Key-Words / Index Term :
Healthcare; Machine Learning; Diabetes; Random forest; Classification

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