Hierarchical Fuzzy Inference System Quantifiable Rule-Based Diagnosis of Radiation Dermatitis with Linguistically Refined Features-Driven Optimization

The expeditious and precise diagnosis of radiation dermatitis (RD) is expected to mitigate the afflictions endured by patients. Dealing with the intricacies inherent in RD data, feature volatilities, inter-feature redundancies, and elusive quantification of their salience, this study propounds a pioneering hierarchical Takagi–Sugeno–Kang fuzzy classification model that can quantitatively eliminate the redundancy between training features, named RDE–T–S. With the introduction of a mechanism for feature optimization alongside a partially rules-stochastic linking strategy, this study endeavors to surmount the challenges posed by feature importance delineation and rule superfluity, thereby fortifying the model's architectural robustness and streamlining the training paradigm. Expounding the original input space, this study establishes a pioneering methodology for optimizing the input space of base building units, thereby facilitating the rapid unfolding of the original manifold structure with the aim of expediting model convergence. In pursuit of maximal classification efficacy, an integrated optimization output mechanism is advanced, harnessing a multiplicationweighted methodology predicated upon the fusion of subjective and objective weight permutations; thus augmenting the adaptability of model parameterization. Of particular note is the emulation prowess of RDE– T–S that adeptly mirrors human cognitive processes, capitalizing on ubiquitous experiential knowledge (rules) to navigate analogous tasks with finesse. The experimental results show that the average training accuracy of RDE–T–S on five datasets reached 95%, which is indeed superior to current popular classifiers. Therefore, it is a promising option for assessing the severity of RD.

Duke Scholars

Author Jing Cai Radiation Oncology