Open Access

ARTICLE

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

by Elias Hossain¹, Mohammed Alshehri², Sultan Almakdi^2,*, Hanan Halawani², Md. Mizanur Rahman³, Wahidur Rahman⁴, Sabila Al Jannat⁵, Nadim Kaysar⁶, Shishir Mia⁴

1 Department of Software Engineering, Daffodil International University, Dhaka, 1207, Bangladesh
2 Department of Computer Science, Najran University, Najran, 55461, Saudi Arabia
3 Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204, Bangladesh
4 Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
5 Department of Computer Science and Engineering, BRAC University, Dhaka, 1212, Bangladesh
6 Department of Computer Science and Engineering, World University Bangladesh, Dhaka, 1230, Bangladesh

* Corresponding Author: Sultan Almakdi. Email:

(This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)

Computers, Materials & Continua 2022, 72(1), 1713-1746. https://doi.org/10.32604/cmc.2022.024822

Received 01 November 2021; Accepted 06 January 2022; Issue published 24 February 2022

Abstract

Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes. The Doctor Consultation Module ensures free treatment related to diabetes. In this research, 90% accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor's algorithm (KNN) & LightGBM. To evaluate the model's performance, Receiver Operating Characteristics (ROC) Curve and Area under the ROC Curve (AUC) were applied with a value of 0.948 and 0.936, respectively. This manuscript presents some exploratory data analysis, including a correlation matrix and survey report. Moreover, the proposed solution can be adjustable in the daily activities of a diabetic patient.

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