Hybrid Fuzzy Deep Learning Model for Personalized Treatment Optimization in Smart Healthcare Systems
Sheela Hundekari
Centre for Smart Systems and Automation, COE for Robotics and Sensing Technologies, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia. and School of Computer Applications, Pimpri Chinchwad University, Pune, Maharashtra, India.
Centre for Smart Systems and Automation, COE for Robotics and Sensing Technologies, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia and Faculty of Artificial Intelligence and Engineering, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia.
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.
Keywords
Deep Learning, Fuzzy Logic, Customised Healthcare, Therapy Optimisation, Smart Healthcare, Medical Decision-Making, Electronic Health Records, Predictive Analytics.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Sheela Hundekari, Yi-Fei Tan, Anurag Shrivastava and Mohammed I Habelalmateen;
Methodology: Sheela Hundekari and Yi-Fei Tan;
Software: Anurag Shrivastava and Mohammed I Habelalmateen;
Data Curation: Sheela Hundekari and Yi-Fei Tan;
Writing- Original Draft Preparation: Sheela Hundekari, Yi-Fei Tan, Anurag Shrivastava and Mohammed I Habelalmateen;
Visualization: Sheela Hundekari and Yi-Fei Tan;
Investigation: Anurag Shrivastava and Mohammed I Habelalmateen;
Supervision: Sheela Hundekari and Yi-Fei Tan;
Validation: Anurag Shrivastava and Mohammed I Habelalmateen;
Writing- Reviewing and Editing: Sheela Hundekari, Yi-Fei Tan, Anurag Shrivastava and Mohammed I Habelalmateen;All authors reviewed the results and approved the final version of the manuscript.
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Sheela Hundekari
Centre for Smart Systems and Automation, COE for Robotics and Sensing Technologies, Multimedia University, 63100 Cyberjaya, Selangor, Malaysia. and School of Computer Applications, Pimpri Chinchwad University, Pune, Maharashtra, India.
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Cite this article
Sheela Hundekari, Yi-Fei Tan, Anurag Shrivastava and Mohammed I Habelalmateen, “Hybrid Fuzzy Deep Learning Model for Personalized Treatment Optimization in Smart Healthcare Systems”, Journal of Machine and Computing, vol.5, no.3, pp. 1628-1641, July 2025, doi: 10.53759/7669/jmc202505129.
