Citation

Hundekari, Sheela and Tan, Yi Fei and Shrivastava, Anurag and Habelalmateen, Mohammed I. (2025) Hybrid Fuzzy Deep Learning Model for Personalized Treatment Optimization in Smart Healthcare Systems. Journal of Machine and Computing, 5 (3). pp. 1628-1641. ISSN 2789-1801

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Abstract

Modern healthcare depends much on personalized treatment optimization, which seeks to improve patient outcomes by customizing medical procedures depending on particular health circumstances. This work presents a hybrid fuzzy-deep learning model (HF-DLM) to maximize treatment plans in smart healthcare systems. Combining fuzzy logic with deep learning, the approach uses deep neural networks for pattern identification and decision-making to manage ambiguity in medical data: While deep learning increases prediction accuracy by automatic feature extraction, the fuzzy component improves interpretability by including expert knowledge. Clinical datasets and actual electronic health records (EHRs) help to assess the proposed HF-DLM. HF-DLM beats traditional machine learning and rule-based systems in forecasting ideal treatment regimens, thereby lowering side effects, and so enhancing patient recovery rates. Comparative study of current methods emphasizes in terms of accuracy, recall, and computing efficiency the benefits of HF-DLM. The paper also addresses issues of implementation including data privacy, model interpretability, and real-time deployment concerns.

Item Type:	Article
Uncontrolled Keywords:	Deep Learning, Fuzzy Logic, Customised Healthcare, Therapy Optimisation, Smart Healthcare, Medical Decision-Making, Electronic Health Records, Predictive Analytics.
Subjects:	R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	29 Jul 2025 03:24
Last Modified:	01 Aug 2025 01:36
URII:	http://shdl.mmu.edu.my/id/eprint/14355

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