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Paper ID:IJSREMWP49318

Implementation of a Web Application to Predict Diabetes Disease using Machine Learning Algorithm

Stream/Area:

-

Subject:

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Paper ID:

IJSREMWP49318

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

TtB

Thirumalai teja B

PC

Prasanth C

DSSP

Dr S. Sathiya Priya

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Abstract

Diabetes is a disease caused by the formation of a disproportionately large amount of sugar in the blood coagulating. Today, it is one of the fatal diseases in the world. This complex illness affects people worldwide both consciously and subconsciously. Diabetes is also the reason for diseases like heart attack, paralyzed, kidney disease, blindness etc. Various type of computer based detection works are proposed in literatures for prediction and simulation of diabetes. Identifying process for diabetic patients usual required much more time and money. However, machine learning is the key to creating a solution to this agitated problem. So we have create another architecture where we have ability to predict whether the patient has diabetes or not. The main objective of this analysis is to create a web application for predicting diabetes of a user through the prediction of a high accuracy by some high power machine learning algorithm. A benchmark dataset namely Pima Indian has been used for prediction of the use diabetes based on a diagnosis. With an accuracy of 82.35%of the predicted speed of artificial neural networks (ANNs), a significant improvement in accuracy has been shown to developinteractive web applications for predicting diabetes.

KEYWORDS

Diabetes SVM ANN Naive Bayes Min Max Scaling

Cite This Article

Thirumalai teja B, Prasanth C, Dr S. Sathiya Priya, "Implementation of a Web Application to Predict Diabetes Disease using Machine Learning Algorithm," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025.

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Publication Date

5/24/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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