Diabetes Predictor: Prediction Using Machine Learning Techniques

A Hariprasad Reddy¹ , G. Tejaswi² , S. Akshay³ , E. Anisha⁴

Section:Research Paper, Product Type: Journal
Vol.12 , Issue.3 , pp.1-6, Jun-2024

Online published on Jun 30, 2024

Copyright © A Hariprasad Reddy, G. Tejaswi, S. Akshay, E. Anisha . 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 :
As the old saying goes, prevention is better than cure when it comes to health. The likelihood of saving lives can be greatly increased by anticipating diseases such as diabetes. Numerous variables, including age, obesity, lack of exercise, genetic predisposition, lifestyle, nutrition, and high blood pressure, can contribute to diabetes, an illness that is spreading quickly. With the help of machine learning techniques (MLT), healthcare professionals can now forecast patient outcomes using pre-existing data, which makes them indispensable tools. Several categorization machine learning methods are used in a diabetes prediction project to identify the most accurate model. This model takes into account extrinsic factors linked to diabetes risk in addition to conventional components like insulin, age, BMI, and glucose. Comprehending the natural glucose regulating process of the body is essential to understanding diabetes. The body uses glucose, which is obtained from foods high in carbohydrates, as its main energy source. The pancreas secretes insulin, which makes glucose easier for cells to use as fuel. On the other hand, diabetes is brought on by inadequate insulin synthesis or inadequate insulin use, which raise blood glucose levels. Here, skin thickness, number of conceptions, and pedigree function are additional characteristics that improve the model`s prediction power. These factors enhance the accuracy of diabetes risk assessment by adding to conventional markers and providing insightful information. Proactive illness prediction is made possible by utilizing MLT in the healthcare industry, especially for conditions like diabetes. The predicted accuracy of diabetes models can be greatly increased by incorporating both traditional and non-conventional risk indicators, such as skin thickness, number of pregnancies, and pedigree function. This will enable early intervention and better patient outcomes.

Key-Words / Index Term :
Prevention, Diabetes, Extrinsic factors, Skin thickness, Conceptions (number of pregnancies), Pedigree function, Proactive prediction, Early intervention, Anticipating, Predisposition, Incorporating, Indispensable.

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