International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-124 March-2025
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A hybrid statistical-fuzzy recommendation system for multi-disease prediction and patient risk assessment

Lakhvinder Singh1 and Dalip2

Research Scholar, Department of Computer Science,M.M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, 133207,India1
Department of Computer Science,M.M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, 133207,India2
Corresponding Author : Lakhvinder Singh

Recieved : 12-August-2024; Revised : 25-March-2025; Accepted : 27-March-2025

Abstract

A smart, information technology (IT)-based system has the potential to enhance the quality of life for patients with serious diseases by providing valuable health advice. This article presents the design and implementation of a hybrid statistical-fuzzy (Hybrid S-Fuzzy) model that offers disease prediction and personalized recommendations for patients with complex conditions, such as heart, liver, and kidney diseases (KDs). The primary objective of this study is to introduce a diagnostic method that accurately verifies a patient's condition and provides precise recommendations. Once the disease prediction is made, the Hybrid S-Fuzzy recommender engine generates medical advice by evaluating the severity of the patient's medical characteristics, the associated risks, and the probability of disease occurrence. The core goal of the Hybrid S-Fuzzy recommendation engine is to develop a personalized recommendation system (RS) using a medical database, which has been compiled and labeled in consultation with healthcare professionals. The performance of the system is assessed using accuracy as a key metric, and the health recommendations provided are based on data collected from online medical sources. The results indicate that the proposed recommendation method achieves an accuracy rate of 96.5%. The implemented model demonstrates high precision in disease prediction and recommendation generation, highlighting its potential contribution to e-health and clinical informatics.

Keywords

Hybrid statistical-fuzzy model, Multi-disease prediction, Personalized health recommendations, Clinical decision support system, Medical data analysis, E-health and clinical informatics.

Cite this article

Singh L, Dalip. A hybrid statistical-fuzzy recommendation system for multi-disease prediction and patient risk assessment. International Journal of Advanced Technology and Engineering Exploration. 2025;12(124):467-488. DOI : 10.19101/IJATEE.2024.111101461

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