Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques
subject: biomarkers; deep learning; feature selection; machine learning; model interpretability; prediction; supervised learning; Type II diabetes; wearable devices
description: Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions. 2025 IEEE.
creator: Kushwah, Virendra Singh; Krishnan, Sivaneasan Bala; Upreti, Kamal; Kumar, Manoj; Kshirsagar, Pravin R.; Singh, Vinai K.
source: 2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.152-156
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833152749-5;
language: English
type: Conference paper
coverage: Kushwah V.S., Singapore Institute of Technology, Singapore; Krishnan S.B., Singapore Institute of Technology, Singapore; Upreti K., School of Sciences Christ University, Department of Computer Science, DelhiNCR, Ghaziabad, India; Kumar M., Gurukula Kangri University, Department of Mathematics and Statistics, Haridwar, India; Kshirsagar P.R., J D College of Engineering & Management, Department of Electronics & Telecommunication Engineering, Nagpur, India; Singh V.K., Gl Bajaj Group of Institutions, Department of Computer Science & Engineering, Mathura, India