Journal of Machine and Computing


Fuzzy Logic Driven Intelligent System for Uncertainty Aware Decision Support Using Heterogeneous Data



Journal of Machine and Computing

Received On : 18 March 2025

Revised On : 12 June 2025

Accepted On : 11 August 2025

Published On : 05 October 2025

Volume 05, Issue 04

Pages : 2672-2687


Abstract


Conventional decision-making models often overlook gene sequence data, limiting their ability to deliver individualized strategies. Precision-focused approaches seek to overcome this limitation by leveraging empirical and computational techniques tailored to unique data profiles. Traditional diagnostic frameworks frequently falter when confronted with uncertainty, vague inputs, and intricate reasoning demands. This study presents an Intelligent Decision System (IDS) powered by Fuzzy Logic (FL), designed to enhance personalized analysis across diverse data types. Unlike rigid rule-based or purely statistical models, FL mirrors human reasoning by accommodating ambiguity and integrating domain expertise into the inference process. The proposed IDS utilizes fuzzy inference systems to process heterogeneous inputs, including genomic variations, behavioral attributes, and quantitative indicators. Through the application of fuzzy rules and membership functions, the system evaluates risk levels and formulates context-sensitive recommendations. Trained on real-world datasets collected up to October 2023 and validated against expert assessments, the IDS demonstrates superior performance in classification accuracy, sensitivity, and specificity in scenarios involving multiple complex conditions such as cancer, diabetes, and cardiovascular anomalies. Transparent and interpretable outputs foster trust and facilitate informed decision-making, positioning the system as a valuable asset in high-stakes analytical environments. This work underscores the promise of fuzzy logic in artificial intelligence, offering a resilient, explainable, and human-aligned framework for navigating uncertainty in data-rich domains. Future integration of deep learning and real-time data processing is anticipated to further elevate predictive capabilities and responsiveness.


Keywords


Fuzzy Logic, Intelligent Decision System, Uncertainty Modeling, Heterogeneous Data Integration, Personalized Strategy Optimization.


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CRediT Author Statement


The authors confirm contribution to the paper as follows:

Conceptualization: Santosh Kumar, Margi Patel, Bipin Bihari Jayasingh, Mohit Kumar, Zaed Balasm and Saloni Bansal; Writing- Original Draft Preparation: Santosh Kumar, Margi Patel, Bipin Bihari Jayasingh, Mohit Kumar, Zaed Balasm and Saloni Bansal; Visualization: Mohit Kumar, Zaed Balasm and Saloni Bansal; Investigation: Santosh Kumar, Margi Patel and Bipin Bihari Jayasingh; Supervision: Mohit Kumar, Zaed Balasm and Saloni Bansal; Validation: Santosh Kumar, Margi Patel and Bipin Bihari Jayasingh; Writing- Reviewing and Editing: Santosh Kumar, Margi Patel, Bipin Bihari Jayasingh, Mohit Kumar, Zaed Balasm and Saloni Bansal; All authors reviewed the results and approved the final version of the manuscript.


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Cite this article


Santosh Kumar, Margi Patel, Bipin Bihari Jayasingh, Mohit Kumar, Zaed Balasm and Saloni Bansal, “Fuzzy Logic Driven Intelligent System for Uncertainty Aware Decision Support Using Heterogeneous Data”, Journal of Machine and Computing, vol.5, no.4, pp. 2672-2687, October 2025, doi: 10.53759/7669/jmc202505205.


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© 2025 Santosh Kumar, Margi Patel, Bipin Bihari Jayasingh, Mohit Kumar, Zaed Balasm and Saloni Bansal. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.