Precision-Driven Pneumonia Diagnosis: Integrating Adaptive Neuro-Fuzzy Inference System (ANFIS) with High-Dimensional Data Analysis
Veera Swamy Pittala, Uppalapati Asritha, Kasthala Ashok Babu, Puritipati Harsha Vardhan Reddy · Advances in computer science research · 2024
This research paper introduces a transformative approach to diagnosing pneumonia through an Adaptive Neuro-Fuzzy Inference System (ANFIS) tailored for high-dimensional clinical data.The ANFIS model fuses the interpretive strengths of fuzzy logic with the adaptive properties of neural networks to process intricate patient data.Our comprehensive evaluation across numerous clinical datasets demonstrates an unprecedented diagnostic accuracy rate exceeding 95%, a precision rate above 90%, and a recall rate equally robust, culminating in an F1 score of 0.92.These metrics, coupled with a ROC-AUC value of 0.98, underscore the model's exceptional capability in discriminating between the nuanced presentations of pneumonia and healthy cases.The findings signal a significant advancement in clinical diagnostics, suggesting the ANFIS model's potential to enhance patient outcomes through precise and reliable pneumonia detection.This integration of neuro-fuzzy systems with machine learning opens new avenues for the development of high-accuracy diagnostic tools, potentially revolutionizing the domain of medical diagnostics and patient care.