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Development of an AI-Driven Medical Diagnostic System for Type-2 Diabetes Detection
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I7-1713849
Abstract
Diabetes is a growing health concern worldwide, and early detection is crucial for effective treatment. This paper introduces a predictive model that combines Support Vector Machine (SVM) and Random Forest algorithms with a soft voting classifier to detect diabetes based on clinical data. The ensemble model leverages the strengths of both SVM and RF to improve overall performance. The methodology follows an Object Oriented Analysis and Design Methodology (OOADM) to model the system architecture and to address class imbalance issues, Synthetic Minority Over-sampling Technique (SMOTE) is employed as a data balancing technique. The proposed approach employs Electronic Health Records (EHR) and a user-friendly interface for efficient data analysis and real-time prediction incorporating attributes such as blood pressure, age, BMI, smoking status, and physical activity to predict diabetes. The results demonstrates the effectiveness of ensemble machine learning in identifying diabetes patients, achieving 96 % of accuracy, 97% of precision, 99% of recall, and 98% of F1-score highlighting its potential for early detection and providing a promising direction for future research and development of robust real-time prediction systems.
Keywords
Diabetes, machine learning, SVM, RF, Electronic Health Records(EHR)
How to cite this paper
@article{1713849,
author = {Igwilo Chioma Goodness, Prof V. E Ejiofor, Dr. S. A Alade, Dr. Rose U. Paul},
title = {Development of an AI-Driven Medical Diagnostic System for Type-2 Diabetes Detection},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {7},
pages = {2324-2335},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713849.pdf},
abstract = {Diabetes is a growing health concern worldwide, and early detection is crucial for effective treatment. This paper introduces a predictive model that combines Support Vector Machine (SVM) and Random Forest algorithms with a soft voting classifier to detect diabetes based on clinical data. The ensemble model leverages the strengths of both SVM and RF to improve overall performance. The methodology follows an Object Oriented Analysis and Design Methodology (OOADM) to model the system architecture and to address class imbalance issues, Synthetic Minority Over-sampling Technique (SMOTE) is employed as a data balancing technique. The proposed approach employs Electronic Health Records (EHR) and a user-friendly interface for efficient data analysis and real-time prediction incorporating attributes such as blood pressure, age, BMI, smoking status, and physical activity to predict diabetes. The results demonstrates the effectiveness of ensemble machine learning in identifying diabetes patients, achieving 96 % of accuracy, 97% of precision, 99% of recall, and 98% of F1-score highlighting its potential for early detection and providing a promising direction for future research and development of robust real-time prediction systems.},
keywords = {Diabetes, machine learning, SVM, RF, Electronic Health Records(EHR)},
month = {January},
doi = {https://doi.org/10.64388/IREV9I7-1713849}
}