Predicting Diabetes in Bangladesh Using Machine Learning: A Data-Driven Approach
Author(s): Tonny Shekha Kar1, Jothirmoy Sarker Shuvo2
Affiliation: 1,2Department of Computer Science, 3Department of Electrical & Electronic Engineering, 1,2,3American International University Bangladesh, Bangladesh
Page No: 39-47-
Volume issue & Publishing Year: Volume 2 Issue 4,April-2025
Journal: International Journal of Modern Engineering and Management | IJMEM
ISSN NO: 3048-8230
DOI:
Abstract:
Diabetes is a chronic condition caused by inadequate insulin synthesis, which prevents the body from processing blood sugar. Diabetes' etiology is still mostly unknown. Furthermore, diabetes is not routinely measured. Since there is no known treatment for diabetes, it is crucial for those who have disease to monitor their blood sugar levels in order to manage and maintain their health. Therefore, safeguards against diabetes or early detection are necessary. Diabetes symptoms can occur suddenly, and it can be mild so it can take years to notice. Diabetes can damage the heart, eyes, kidneys, nerves and damage blood vessels over time. As of today, no known means to prevent diabetes nor its cause are known so early diagnosis or predicting diabetes is necessary to prevent the worst effects of diabetes. Additionally, People in Bangladesh are still ignorant about diabetes and unaware exactly when diabetes has to be measured. In this paper, we are going to predict diabetes by using machine learning. We compared conventional machine learning with deep learning approaches. For the conventional machine learning method, we considered the most commonly used classifiers: K-Nearest Neighbors (KNN) and Random Forest (RF). On the other hand, for Deep Learning (DL) we employed a fully Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) Algorithm to predict and detect diabetes patients.
Keywords:
Artificial Neural Networks, Diabetes Prediction, Deep Learning, Random Forest.
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