Medical care relies heavily on the Intelligent Health Recommendation System (HRS). Effective healthcare networks are therefore essential to the process of making medical decisions. The key goal is to maintain the efficacy of information security and ethical considerations while ensuring appropriateness. Healthcare recommendation systems must produce results such as diagnosis suggestions, healthcare insurance, and information related to health issues. This article highlights the hospital authority’s capacity to provide a personalized recommendation system and offers a Fuzzy Intelligent Recommendations System (FIRS). The input patient data is analyzed using a Modified Fuzzy Rule-based Neural Network (MFRNN) to predict potential illnesses. In the intelligent healthcare system, the data privacy is maintained using Multi-Criteria-based Decision Making (MCDM) and Fuzzy assisted Analytical Hierarchy Process (Fuzzy-AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)). The suggested fuzzy intelligent system outperformed previous methods using larger datasets, which only managed a 97.75% prediction accuracy. Furthermore, when contrasted with conventional models, the MFRNN showed much lower error rates and better reliability in risk assessment. The security risk study’s findings show that the proposed fuzzy model has the potential to deliver the best risk assessment performance compared to other models.

[1] Abdel-Basset M., Hawash H., Chakrabortty R., Ryan M., and et al., “STDeepHAR: Deep Learning Model for Human Activity Recognition in IoHT Applications,” IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4969-4979, 2021. https://doi.org/10.1109/JIOT.2020.3033430

[2] Ahmad F., Ali L., Mustafa R., Khattak H., and et al., “A Hybrid Machine Learning Framework to Predict Mortality in Paralytic Ileus Patients Using Electronic Health Records,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 3283-3293, 2020. https://doi.org/10.1007/s12652-020-02456-3

[3] Akhtar Y., Ali M., Almaliki F., and Almarzouki R., “A Novel IoT-Based Approach Using Fractional Fuzzy Hamacher Aggregation Operators Application in Revolutionizing Healthcare Selection,” Scientific Reports, vol. 15, Application of Fuzzy Decision Intelligent Recommendation System in Personalized ... 79 pp. 1-27, 2025. https://doi.org/10.1038/s41598- 024-83805-6

[4] Bellahcene M., Benamar F., and Mekidiche M., “AHP and WAFGP Hybrid Model for Information System Project Selection,” International Journal of the Analytic Hierarchy Process, vol. 12, no. 2, pp. 228-253, 2020. https://doi.org/10.13033/ijahp.v12i2.761

[5] Bouayad L., Padmanabhan B., and Chari K., “Can Recommender Systems Reduce Healthcare Costs? The Role of Time Pressure and Cost Transparency in Prescription Choice,” Management Information Systems Quarterly, vol. 44, no. 4, pp. 1859-903, 2020. https://doi.org/10.25300/MISQ/2020/14435

[6] Chakradar M., Aggarwal A., Cheng X., Rani A., and et al., “A Non-Invasive Approach to Identify Insulin Resistance with Triglycerides and HDL-C Ratio Using Machine Learning,” Neural Processing Letters, vol. 55, pp. 93-113, 2023. https://doi.org/10.1007/s11063-021-10461-6

[7] Chen C. and Chu C., “A Fuzzy Method for Exploring Key Factors of Smart Healthcare to Long-Term Care Based on Z-Numbers,” Mathematics, vol. 12, no. 22, pp. 3471, 2024. https://doi.org/10.3390/math12223471

[8] Csiszar O., Csiszar G., and Dombi J., “How to Implement MCDM Tools and Continuous Logic into Neural Computation? Towards Better Interpretability of Neural Networks,” Knowledge- Based Systems, vol. 210, pp. 106530, 2020. https://doi.org/10.1016/j.knosys.2020.106530

[9] Gavurova B., Kelemen M., Polishchuk V., Mudarri T., and Smolanka V., “A Fuzzy Decision Support Model for the Evaluation and Selection of Healthcare Projects in the Framework of Competition,” Frontiers in Public Health, vol. 11, pp. 1-15, 2023. https://doi.org/10.3389/fpubh.2023.1222125

[10] Ghosh A. and Kar S., “Application of Analytical Hierarchy Process for Flood Risk Assessment: A Case Study in Malda District of West Bengal,” Natural Hazards, vol. 94, pp. 349-368, 2018. https://doi.org/10.1007/s11069-018-3392-y

[11] Gu D., Sun D., Muthu B., Hsu C., “Regional Electromagnetic Actuation Simulation and Monitoring for Robotically Aided Surgical Equipment with Medical Platform,” Measurement, vol. 168, pp. 108248. https://doi.org/10.1016/j.measurement.2020.1082 48

[12] Gul M., “A Review of Occupational Health and Safety Risk Assessment Approaches Based on Multi-Criteria Decision-Making Methods and their Fuzzy Versions,” Human and Ecological Risk Assessment: An International Journal, vol. 24, no. 7, pp. 1723-1760, 2018. https://doi.org/10.1080/10807039.2018.1424531

[13] Hassan B. and Elagamy S., “Personalized Medical Recommendation System with Machine Learning,” Neural Computing and Applications, vol. 37, pp. 6431-6447, 2025. https://doi.org/10.1007/s00521- 024-10916-6

[14] Kadiravan G., Sujatha P., Asvany T., Punithavathi R., and et al., “Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks,” Computers Materials and Continua, vol. 66, no. 3, pp. 3215- 3231, 2021. https://doi.org/10.32604/cmc.2021.013034

[15] Mani V., Kavitha C., Band S., Mosavi A., and et al., “A Recommendation System Based on AI for Storing Block Data in the Electronic Health Repository,” Frontiers in Public Health, vol. 9, pp. 1-12, 2021. https://doi.org/10.3389/fpubh.2021.831404

[16] Manogaran G., Alazab M., Shakeel M., and Hsu C., “Blockchain Assisted Secure Data Sharing Model for Internet of Things Based Smart Industries,” IEEE Transactions on Reliability, vol. 71, no. 1, pp. 348-358, 2022. https://doi.org/10.1109/TR.2020.3047833

[17] Mitra R. and Das J., “A Comparative Assessment of Flood Susceptibility Modelling of GIS-Based TOPSIS, VIKOR, and EDAS Techniques in the Sub-Himalayan Foothills Region of Eastern India,” Environmental Science and Pollution Research, vol. 30, no. 6, pp. 16036-16067, 2023. https://link.springer.com/article/10.1007/s11356- 022-23168-5

[18] Nguyen P., Huynh V., Vo K., Phan P., and et al., “Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data,” Computers Materials and Continua, vol. 66, no. 3, pp. 2555- 2571, 2020. https://doi.org/10.32604/cmc.2021.012941

[19] Ochoa J., Csiszar O., and Schimper T., “Medical Recommender Systems Based on Continuous- Valued Logic and Multi-Criteria Decision Operators, Using Interpretable Neural Networks,” BMC Medical Informatics and Decision Making, vol. 21, no. 186, pp. 1-15, 2021. https://doi.org/10.1186/s12911-021-01553-3

[20] Ponnusamy C., Wong W., Raja A., Khalaf O., and et al., “Health Recommendation System Using Deep Learning-Based Collaborative Filtering,” Heliyon, vol. 9, pp. 1-16, 2023. https://doi.org/10.1016/j.heliyon.2023.e22844

[21] Quasim M., Shaikh A., Shuaib M., Sulaiman A., and et al., “Smart Healthcare Management Evaluation Using Fuzzy Decision Making Method,” Research Square, vol. 1, pp. 1-19, 2021. https://doi.org/10.21203/rs.3.rs-424702/v1

[22] Rajaram S., “A Model for Real-Time Heart Condition Prediction Based on Frequency Pattern Mining and Deep Neural Networks,” PatternIQ 80 The International Arab Journal of Information Technology, Vol. 23, No. 1, January 2026 Mining, vol. 1, no. 1, pp. 1-11, 2024. https://doi.org/10.70023/piqm241

[23] Sahoo A., Pradhan C., Barik R., and Dubey H., “DeepReco: Deep Learning-Based Health Recommender System Using Collaborative Filtering,” Computation, vol. 7, no. 25, pp. 1-18, 2019. https://doi.org/10.3390/computation7020025

[24] Sharma V. and Samant S., “Health Recommendation System by Using Deep Learning and Fuzzy Technique,” in Proceedings of the Advancement in Electronics and Communication Engineering, Delhi, pp. 1-7, 2022. https://papers.ssrn.com/sol3/papers.cfm?abstract_ id=4159847

[25] Shaygan A. and Testik O., “A Fuzzy AHP-Based Methodology for Project Prioritization and Selection,” Soft Computing, vol. 23, pp. 1309- 1319, 2019. https://doi.org/10.1007/s00500-017- 2851-9

[26] Tabarestani E. and Afzalimehr H., “A Comparative Assessment of Multi-Criteria Decision Analysis for Flood Susceptibility Modelling,” Geocarto International, vol. 37, no. 20, pp. 5851-5874, 2022. https://doi.org/10.1080/10106049.2021.1923834

[27] Zhao J., Xi X., Na Q., Wang S., and et al., “The Technological Innovation of Hybrid and Plug-in Electric Vehicles for Environment Carbon Pollution Control,” Environmental Impact Assessment Review, vol. 86, pp. 106506, 2021. https://doi.org/10.1016/j.eiar.2020.106506