Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kushwah, Virendra Singh; Krishnan, Sivaneasan Bala; Upreti, Kamal; Kumar, Manoj; Kshirsagar, Pravin R.; Singh, Vinai K. Title A name given to the resource Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.152-156 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC363308.2025.10956963" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC363308.2025.10956963</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003908592?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003908592?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; Management, Department of Electronics &amp; Telecommunication Engineering, Nagpur, India; Singh V.K., Gl Bajaj Group of Institutions, Department of Computer Science &amp; Engineering, Mathura, India Description An account of the resource 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. Subject The topic of the resource biomarkers; deep learning; feature selection; machine learning; model interpretability; prediction; supervised learning; Type II diabetes; wearable devices Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152749-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online