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.
Kumar et al. (Mon,) studied this question.