Machine Learning Based Diabetes Prediction System: A Novel Approach

Shalini Shekhar; Dr. Nikita Thakur

doi:10.55524/ijirem.2023.10.6.14

Abstract

The healthcare sector is poised to experience a remarkable transformation with the integration of artificial intelligence. In the realm of healthcare analysis and prediction, the utilization of data science and machine learning applications proves advantageous. Healthcare is emerging as a progressive and promising field for the implementation of data science applications, particularly in Medical Images Analysis, Drug Discovery, Genetics Research, and Predictive Medicine. Diabetes is broadly classified into three main types: type 1, type 2, and gestational diabetes. The primary objective of this research is to develop a Machine Learning Model for the diagnosis of diabetes. Identifying the accurate symptoms in users or individuals with diabetes is a significant challenge for application and the execution of rules. These combinations of knowledge determine whether an individual is a diabetes patient, including its subtypes such as type_1, type_2, and gestational diabetes. The Machine Learning Model underwent testing on a cohort of 150 patients, producing results comparable to those of medical professionals.

Keywords

Diabetes Prediction System, Machine Learning, Classification Models.

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Cites this article as

[Shalini Shekhar, Dr. Nikita Thakur (2023), Machine Learning Based Diabetes Prediction System: A Novel Approach, International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-10, Issue-6, Page No-92-99], (ISSN 2347 - 5552). www.ijirem.org