Enhancing meningitis diagnosis accuracy through the integration of fuzzy logic and random forest: A conceptual framework

Meningitis, an inflammation of the meninges surrounding the brain and spinal cord, presents a significant challenge in clinical diagnosis due to its diverse etiology and varied symptom presentation. It remains a significant health concern globally, particularly in Africa, where it claims the lives of hundreds of thousands annually. This paper proposes a hybrid approach to enhance diagnostic accuracy by integrating a fuzzy classifier with the Random Forest algorithm. Fuzzy logic is well-suited for handling uncertainty and imprecision inherent in medical data, while random forest offers robustness in handling high-dimensional datasets and ensemble learning benefits. This integration not only holds promise for heightened diagnostic accuracy but also facilitates interpretability and explainability of outcomes crucial for clinical decision-making. By addressing a critical healthcare challenge, this conceptual framework offers the synergistic fusion of fuzzy classifier and Random Forest techniques, with the aim of advancing meningitis diagnosis accuracy and laying the groundwork for further innovation in medical diagnostics.