{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:19:42Z","timestamp":1777360782695,"version":"3.51.4"},"reference-count":18,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses the inherent uncertainty and variability in healthcare data. This study demonstrates the real-world applicability of BNNs through three key case studies: personalized diabetes treatment, early Alzheimer\u2019s disease detection, and predictive modeling for HbA1c levels. By leveraging the Bayesian approach, these models provide not only enhanced predictive accuracy but also uncertainty quantification, a critical factor in clinical decision making. While the findings are promising, future research should focus on optimizing scalability and integration for real-world applications. This work lays a foundation for future studies, including the development of rating scales based on BNN predictions to improve clinical outcomes.<\/jats:p>","DOI":"10.3390\/make6040127","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T10:37:35Z","timestamp":1732099055000},"page":"2639-2658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Application of Bayesian Neural Networks in Healthcare: Three Case Studies"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0561-1305","authenticated-orcid":false,"given":"Lebede","family":"Ngartera","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of N\u2019Djamena, N\u2019Djamena BP 1117, Chad"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2649-4961","authenticated-orcid":false,"given":"Mahamat Ali","family":"Issaka","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of N\u2019Djamena, N\u2019Djamena BP 1117, Chad"}]},{"given":"Saralees","family":"Nadarajah","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Manchester, Manchester M13 9PL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"ref_1","first-page":"5574","article-title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","volume":"30","author":"Kendall","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_2","first-page":"1050","article-title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","volume":"48","author":"Gal","year":"2016","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Neal, R.M. (1996). Bayesian Learning for Neural Networks, Springer.","DOI":"10.1007\/978-1-4612-0745-0"},{"key":"ref_4","first-page":"256","article-title":"Optimization and Analysis of the Index of Air Quality in Dakar by ARMA(2,1)","volume":"49","author":"Ngartera","year":"2015","journal-title":"Int. J. Appl. Math."},{"key":"ref_5","first-page":"312","article-title":"Modeling and Prediction of Dakar Air Quality Index","volume":"55","author":"Ngartera","year":"2016","journal-title":"Int. J. Appl. Math. Stat."},{"key":"ref_6","first-page":"33","article-title":"Uncertainty Quantification in AI-driven Diagnostics","volume":"72","author":"Kumar","year":"2023","journal-title":"Int. J. Health Inform."},{"key":"ref_7","first-page":"100","article-title":"Bayesian Neural Networks for Healthcare Applications","volume":"85","author":"Nguyen","year":"2023","journal-title":"J. Comput. Intell."},{"key":"ref_8","first-page":"30","article-title":"Deep Gaussian Processes for Hierarchical Healthcare Data","volume":"77","author":"Liu","year":"2023","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_9","first-page":"125","article-title":"Bayesian LSTMs for Predicting Disease Progression","volume":"31","author":"Cheng","year":"2023","journal-title":"J. Health Inform. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e051458","DOI":"10.1136\/bmjopen-2021-051458","article-title":"Reopening Italy\u2019s Schools in September 2020: A Bayesian Estimation of the Change in the Growth Rate of New SARS-CoV-2 Cases","volume":"11","author":"Casini","year":"2021","journal-title":"BMJ Open"},{"key":"ref_11","first-page":"e123456","article-title":"Understanding SARS-CoV-2 Transmission in Healthcare Settings","volume":"374","author":"Smith","year":"2021","journal-title":"BMJ"},{"key":"ref_12","first-page":"980","article-title":"Bayesian Deep Learning for Predictive Modeling in Healthcare","volume":"34","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_13","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_14","unstructured":"Fortunato, M., Blundell, C., and Vinyals, O. (2017). Bayesian Recurrent Neural Networks. arXiv."},{"key":"ref_15","first-page":"166","article-title":"The Norwegian Visual Analog Scale for Activity Questionnaire: A Study of Reliability and Validity in Patients with Traumatic Brain Injury","volume":"20","author":"Soberg","year":"2013","journal-title":"Scand. J. Occup. Ther."},{"key":"ref_16","first-page":"286","article-title":"Rating Scales and Rubrics: Problems and Solutions","volume":"19","author":"Wainer","year":"2014","journal-title":"Educ. Assess."},{"key":"ref_17","first-page":"3159","article-title":"Scaling Deep Learning Models for Healthcare Applications: A Case Study","volume":"32","author":"Huang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_18","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015, January 6\u201311). Weight Uncertainty in Neural Networks. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/4\/127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:33:30Z","timestamp":1760114010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/4\/127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"references-count":18,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["make6040127"],"URL":"https:\/\/doi.org\/10.3390\/make6040127","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,16]]}}}