CFP last date
21 July 2025
Reseach Article

Toward Smart Biosensing: A Machine Learning Approach for Early Diabetes Detection

by Justine Aku Azigi, Frederick Adrah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 19
Year of Publication: 2025
Authors: Justine Aku Azigi, Frederick Adrah
10.5120/ijca2025925268

Justine Aku Azigi, Frederick Adrah . Toward Smart Biosensing: A Machine Learning Approach for Early Diabetes Detection. International Journal of Computer Applications. 187, 19 ( Jul 2025), 8-11. DOI=10.5120/ijca2025925268

@article{ 10.5120/ijca2025925268,
author = { Justine Aku Azigi, Frederick Adrah },
title = { Toward Smart Biosensing: A Machine Learning Approach for Early Diabetes Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 19 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 8-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number19/toward-smart-biosensing-a-machine-learning-approach-for-early-diabetes-detection/ },
doi = { 10.5120/ijca2025925268 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-09T01:07:44.272125+05:30
%A Justine Aku Azigi
%A Frederick Adrah
%T Toward Smart Biosensing: A Machine Learning Approach for Early Diabetes Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 19
%P 8-11
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetes is a global metabolic disorder characterized by impaired glucose metabolism, leading to hyperglycemia and severe complications if untreated. With 1 in 10 Americans affected and rising incidence among youth, early detection is critical. Traditional diagnostic methods, though effective, face limitations in scalability and human error. This study proposes a machine learning (ML) framework for early diabetes prediction using the Behavioral Risk Factor Surveillance System (BRFSS) 2015 dataset (N=70,692), balanced with 50% diabetic cases. We analyze 22 features spanning clinical indicators (e.g., HighBP, HighChol, BMI), lifestyle factors (smoking, exercise), and socioeconomic variables (income, education). Feature engineering introduces interaction terms (BMI×GenHlth, Age×PhysHlth), aggregated chronic conditions, and binned health metrics. Correlation analysis reveals key predictors: HighBP (r=0.38), GenHlth (r=0.32), BMI (r=0.29), and Age (r=0.28), while physical activity and education exhibit protective effects (r=−0.16 to −0.22). Multi-collinearity is observed between health constructs (e.g., GenHlth–PhysHlth: r=0.55). Three ensemble models (Random Forest, XGBoost, LightGBM) consistently rank GenHlth, BMI, and chronic conditions as top predictors. Our approach demonstrates how engineered features enhance ML performance, offering a scalable tool for identifying at-risk individuals missed by conventional screening. This work underscores AI’s potential to transform diabetes surveillance through computational biosensing, bridging gaps in preventive healthcare.

References
  1. G. Swapna, R. Vinayakumar, and K. Soman, “Diabetes detection using deep learning algorithms,” ICT express, vol. 4, no. 4, pp. 243–246, 2018.
  2. D. Campagna et al., “Smoking and diabetes: dangerous liaisons and confusing relationships,” Diabetology & metabolic syndrome, vol. 11, no. 1, pp. 1–12, 2019.
  3. T. Nakahara et al., “Type 2 diabetes mellitus is associated with the fibrosis severity in patients with nonalcoholic fatty liver disease in a large retrospective cohort of Japanese patients,” Journal of gastroenterology, vol. 49, pp. 1477–1484, 2014.
  4. T. Sharma and M. Shah, “A comprehensive review of machine learning techniques on diabetes detection,” Visual Computing for Industry, Biomedicine, and Art, vol. 4, no. 1, p. 30, 2021.
  5. N. Abdulhadi and A. Al-Mousa, “Diabetes detection using machine learning classification methods,” presented at the 2021 international conference on information technology (ICIT), IEEE, 2021, pp. 350–354.
  6. I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine learning and data mining methods in diabetes research,” Computational and structural biotechnology journal, vol. 15, pp. 104–116, 2017.
  7. V. Jaiswal, A. Negi, and T. Pal, “A review on current advances in machine learning based diabetes prediction,” Primary Care Diabetes, vol. 15, no. 3, pp. 435–443, 2021.
  8. J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. S. Johannes, “Using the ADAP learning algorithm to forecast the onset of diabetes mellitus,” presented at the Proceedings of the annual symposium on computer application in medical care, 1988, p. 261.
  9. W. Yu, T. Liu, R. Valdez, M. Gwinn, and M. J. Khoury, “Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes,” BMC medical informatics and decision making, vol. 10, pp. 1–7, 2010.
  10. M. Kalpana and A. S. Kumar, “Fuzzy expert system for diabetes using fuzzy verdict mechanism,” International Journal of Advanced Networking and Applications, vol. 3, no. 2, p. 1128, 2011.
  11. R. Priya and P. Aruna, “Diagnosis of diabetic retinopathy using machine learning techniques,” ICTACT Journal on soft computing, vol. 3, no. 4, pp. 563–575, 2013.
  12. T. Daghistani and R. Alshammari, “Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes,” Journal of Advances in Information Technology Vol, vol. 11, no. 2, pp. 78–83, 2020.
Index Terms

Computer Science
Information Sciences
Chronic conditions
machine learning
health indicators

Keywords

Diabetes prediction feature engineering preventive healthcare biosensing