{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:16:51Z","timestamp":1772122611121,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KLEF Deemed to be University","award":["25001"],"award-info":[{"award-number":["25001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The global healthcare system faces significant challenges posed by diabetes and its complications, highlighting the need for innovative strategies to improve early diagnosis and treatment. Machine learning models help in the early detection of diseases and recommendations for taking safety measures and treating the disease. A comparative analysis of existing machine learning (ML) models is necessary to identify the most suitable model while uniformly fixing the model parameters. Assessing risk based on biomarker measurement and computing overall risk is important for accurate prediction. Early prediction of complications that may arise, based on the risk of diabetes and biomarkers, using machine learning models, is key to helping patients. In this paper, a comparative model is presented to evaluate ML models based on common model characteristics. Additionally, a risk assessment model and a prediction model are presented to help predict the occurrence of complications. Random Forest (RF) is the best model for predicting the occurrence of Type 2 Diabetes (T2D) based on biomarker input. It has also been shown that the prediction of diabetes complications using neural networks is highly accurate, reaching a level of 98%.<\/jats:p>","DOI":"10.3390\/computers14070277","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T11:52:58Z","timestamp":1752580378000},"page":"277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Risk and Complications of Diabetes Through Built-In Artificial Intelligence"],"prefix":"10.3390","volume":"14","author":[{"given":"Siana Sagar","family":"Bontha","sequence":"first","affiliation":[{"name":"Department of IoT, Koneru Lakshmaiah Deemed to be University, Vaddeswaram, Guntur 522501, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9271-6839","authenticated-orcid":false,"given":"Sastry Kodanda Rama","family":"Jammalamadaka","sequence":"additional","affiliation":[{"name":"Department of IoT, Koneru Lakshmaiah Deemed to be University, Vaddeswaram, Guntur 522501, India"}]},{"given":"Chandra Prakash","family":"Vudatha","sequence":"additional","affiliation":[{"name":"Department of IoT, Koneru Lakshmaiah Deemed to be University, Vaddeswaram, Guntur 522501, India"}]},{"given":"Sasi Bhanu","family":"Jammalamadaka","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, CMR College of Engineering and Technology, Hyderabad 501401, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6236-3339","authenticated-orcid":false,"given":"Balakrishna Kamesh","family":"Duvvuri","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad 500043, India"}]},{"given":"Bala Chandrika","family":"Vudatha","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, Green Witch University, London SE10 9LS, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","unstructured":"WHO (2025, January 01). 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