Adaptive Neuro-Fuzzy Inference System (ANFIS) for Clinical Decision Support in Remote Patient Monitoring

The ANFIS combines the learning capabilities of neural networks with the human-like reasoning of fuzzy logic, enabling intelligent decision-making. In remote patient monitoring, ANFIS can play a crucial role in providing timely and accurate clinical support. Existing methods often struggle with real-time analysis, low interpretability, and limited adaptability to dynamic physiological changes, resulting in delayed or inaccurate diagnoses. To address these challenges, a novel ANFIS-Based Hybrid Decision Support System (ANFIS-BHDSS) is proposed, which integrates wearable IoT sensor data with ANFIS for continuous, adaptive, and explainable clinical decision-making. The proposed system monitors vital signs, including heart rate, temperature, and blood pressure. It infers risk levels for critical conditions, including sepsis, enabling early intervention through automated alerts and recommendations. This approach enhances the accuracy and reliability of decision support in remote settings while maintaining transparency for clinical validation. Experimental results demonstrate improved performance in detecting early warning signs with higher sensitivity and specificity compared to traditional models. The proposed method proves effective in enhancing patient safety, reducing hospitalization rates, and supporting healthcare providers in making proactive clinical decisions.

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