In this paper, we present an enhanced fuzzy k-nearest neighbor (FKNN) classifier based computer aided diagnostic (CAD) system for thyroid disease. The neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the particle swarm optimization (PSO) approach. The adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. In addition, we have validated the effectiveness of the principle component analysis (PCA) in constructing a more discriminative subspace for classification. The effectiveness of the resultant CAD system, termed as PCA-PSO-FKNN, has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation (CV) analysis, with the mean accuracy of 98.82% and with the maximum accuracy of 99.09%. Promisingly, the proposed CAD system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.
It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO) to optimize two key parameters (penalty coefficient and the kernel width) of a kernel extreme learning machine (KELM) and build an IBFO-based KELM (IBFO-KELM) for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90) is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model), GA-KELM (model based on genetic algorithm), PSO-KELM (model based on particle swarm optimization algorithm) and Grid-KELM (model based on grid search method). The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC), sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.
In this paper, we present a novel predictive model based on the kernel extreme learning machine (KELM) to predict the somatization disorder. Since the classification performance of KELM is largely affected by its two parameters, it is necessary to set two optimal parameters for it to ensure high prediction accuracy. In order to improve the accuracy of the prediction model, a new optimization strategy is used to optimize the parameters of KELM. The new optimization strategy adopted grey wolf optimization algorithm to generate high-quality initial populations for moth-flame optimization algorithm, called GWOMFO. The effectiveness of GWOMFO was first verified on the ten classic benchmark functions. The results show that the GWOMFO has provided consistently better results than other competitive algorithms. This reveals that high-quality initial populations can significantly improve the global search ability and convergence speed of search agents. Furthermore, the proposed GWOMFO-based KELM model was compared with other models, including a model based on GWO (GWO-KELM), a model based on MFO (MFO-KELM), a model based on genetic algorithm (GA-KELM), a model based on grid search method (Grid-KELM), a random forest, and the support vector machines, on the somatization disorder dataset. The simulation results show that the developed framework cannot only achieve higher prediction accuracy than other models but also has better robustness. INDEX TERMS Grey wolf optimization, moth-flame optimization, kernel extreme learning machine, somatization disorder diagnosis.
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