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For nasopharyngeal cancer (NPC) recognition, how to build an interpretable lightweight recognition model is a serious challenge. Traditional medical diagnostic models typically exhibit opaque decision-making processes with limited interpretability, and achieving high classification accuracy often necessitates increased model complexity. In order to address these limitations of traditional methods, the study proposes an innovative stacked hierarchical Takagi\u2013Sugeno\u2013Kang (TSK) fuzzy classifier for diagnosing the severity of RD in NPC patients. The classifier incorporates a low-rank rule feature structure and bidirectional approximation consequents, with each sub-classifier constructed from first-order TSK fuzzy systems. Its antecedent part employs a rule-feature matrix to characterize relationships between fuzzy rules and training features, where matrix decomposition techniques are applied to simplify rule-feature associations and generate representative discriminative rules. The consequent part integrates outputs from preceding sub-classifiers into the objective function, achieving approximation of both prior sub-classifier outputs and target outputs through independent training mechanisms. Finally, comparative experiments demonstrate that each sub-classifier in LRR-TSK requires only 3\u201315 fuzzy rules yet achieves an average accuracy exceeding 99.6%, highlighting its excellent performance. These improvements stem from the low-rank rule-feature matrix, which generates clear rules via matrix decomposition, and the bidirectional approximation method, which optimizes prediction accuracy, providing a transparent and efficient solution for handling complex medical data. However, the current training models still face the predicament that it is difficult to obtain the hyperparameter combinations of the models and the number of fuzzy rules is hard to determine. In future work, we aim to address these issues by integrating global optimization algorithms to achieve a global optimum solution, and extending the model into a multi-view joint learning framework to enhance diagnostic comprehensiveness.<\/jats:p>","DOI":"10.1142\/s021800142540004x","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T06:02:59Z","timestamp":1752732179000},"source":"Crossref","is-referenced-by-count":0,"title":["A Hierarchical Fuzzy Classifier for Radiation Dermatitis Diagnosis Based on Rule-Feature Reconstruction and Specific Approximation"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5891-9919","authenticated-orcid":false,"given":"Wei","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Computing, Jiangsu University of Science and Technology, No. 666, Changhui Road, Dantu District Zhenjiang, Jiangsu Province, P. R. 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