Hybrid weighted fuzzy production rule extraction utilizing modified harmony search and BPNN
Feng Qin, Azlan Mohd Zain, Kai-qing ZHOU, De-Bing Zhuo
Abstract
Weighted Fuzzy Production Rules (WFPRs) are vital for Clinical Decision Support Systems (CDSSs), significantly impacting diagnostic accuracy and bridging the gap between data-driven insights and actionable clinical decisions through knowledge engineering. This paper proposes an integrated approach combining the Dynamic Dimension Adjustment Harmony Search (DDA-HS) Algorithm and Back Propagation Neural Networks (BPNNs) to enhance WFPR extraction accuracy. DDA-HS dynamically adjusts search space dimensions through fitness evaluations, optimizing initial weights in BPNNs and leveraging an absorbing Markov chain to enhance transition probabilities, supporting exploration and avoiding local optima in high-dimensional spaces. Evaluated against existing optimization methods including Harmony Search (HS), Cuckoo Search (CS), Adaptive Global Optimal Harmony Search (AGOHS), and Harmony Search with Cuckoo Search (HSCS) Algorithms, DDA-HS achieves 74.48% accuracy for BPNN classification and 77.08% for WFPR classification on the PIMA dataset, representing improvements of 3.6% and 6.5%, respectively. WFPR extraction enhances BPNN interpretability by revealing feature influences on decision-making, improving both accuracy and transparency. The proposed method offers a robust framework for reliable and interpretable CDSSs in healthcare.