Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes - A Pilot Study

Aug 5, 2026

Diabetes is a major global health concern with rising prevalence, making regular blood glucose monitoring essential for treatment and care planning. Expanding digital health has generated large datasets that can support ongoing management of this chronic condition. This pilot study reviews how deep learning, a branch of machine learning, has been applied to diabetes research, using a comprehensive literature search and analyzing model details, development approaches, primary outcomes, and baseline evaluation techniques from selected publications. Findings indicate deep learning often surpasses traditional methods, while also revealing gaps such as limited accessible data and limited interpretability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Diabetes and its Many Forms

Type 1 diabetes (T1D)

Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes: A Pilot Study Reehana Sk1 1Research Scholar, School of Computer Science and Engineering, VIT AP-University, Amaravati, Andhra Pradesh, India email: [skreehana.15@gmail.com](skreehana.15@gmail.com) Dr. Siddique Ibrahim S2* *2Assistant Professor, School of Computer Science and Engineering, VIT AP-University, Amaravati, Andhra Pradesh, India Correspondence author: * email: [siddique.ibrahim@vitap.ac.in](siddique.ibrahim@vitap.ac.in) Abstract—Diabetes currently one of the most significant worldwide concerns, and its prevalence is only expected to increase in the future years. In order to monitor glucose levels in the blood and set treatment protocols for diabetes, keeping a regular schedule for checking blood glucose levels is essential. The purpose of widespread adoption of digital health in recent years has been to enhance diabetic healthcare for patients, and as a result, a massive quantity of data has been collected that may be used in the ongoing management of this chronic condition. Deep learning, a relatively new kind of machine learning, is one method that has taken advantage of this trend, and its applications seem promising. In this research, we provide a thorough analysis of how deep learning has been used in the study of diabetes thus far. We conducted a comprehensive literature search and found that this method is most often used in the following settings: diabetes diagnosis, glucose control, and the identification of diabetes-related complications. We have described the most important details regarding the learning models used, the development process, the primary outcomes, and the baseline techniques for performance assessment from the 40 original research publications that we selected based on the search. In the reviewed literature, it becomes clear that several deep learning algorithms and frameworks have outperformed traditional machine learning methods to attain state-of-the-art performance on numerous problems involving diabetes. However, we point out several gaps in the existing literature, such as a dearth of readily available data and a lack of clarity in the interpretation of models. In the near future, these obstacles may be surmounted thanks to the fast advancements in deep learning methodologies which will allow for wider application of this technology in therapeutic settings. Keywords-Diabetes, Glucose, Diabetic Complications, Deep Learning, CNN. I. INTRODUCTION Diabetes mellitus is a metabolic illness may be brought on by either a deficiency of insulin production or by insulin resistance and insufficient insulin synthesis. Both of these scenarios result in excessively high blood sugar levels [1] . Since it is a metabolic condition that worsens over time and impacts the patient in every aspect of their life, including their physical and emotional well-being, there is no treatment modality that can create remarkable improvements or halt the disease's progression [2] . In 2000, India had the highest number of diabetes (31.7 million); by 2011, that figure had risen to 62.4 million, and it is expected to rise to 69.9 million by 2025 [3] . India has rapid population and economic expansion are to blame for the country's high frequency. Indian’s have a greater chance of acquiring diabetes than the general population does as a result of their abdominal obesity, low Body mass index (BMI), high percentage of body fat, and high insulin resistance [4] . Diabetes may lead to an assortment of signs and symptoms, including but not limited to blurred vision, weight loss, tiredness, raised appetite including dryness, disorientation, urine retention, poor healing, and continual infections. Diabetes refers to a condition in which there is an excessive amount of sugar in the blood. The onset of difficulties associated with elevated sugar levels occurs whenever the human body is unable to produce a chemical or hormo

FAQs

Why is regular blood glucose monitoring important in diabetes care?

Regular checking helps track glucose levels and supports timely decisions for diabetes treatment protocols and management of the disease over time.

What research areas does the study highlight for deep learning applications in diabetes?

The review focuses on diabetes diagnosis, glucose control, and identification of diabetes-related complications.

What limitations in existing diabetes deep learning literature are mentioned?

The study points out limited availability of readily accessible data and insufficient clarity in interpreting model results.

Aug 5, 2026

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