## 3 Proposed IBFO-KELM Model
The main reason for this is that the opposite learning strategy plays a role in regulating the diversity of the population so as to accelerate the convergence of the whole population to the optimal solution. The original BFO-KELM model requires 24 iterations to converge to the optimal solution, and the obtained solution is less than that of IBFO. The PSO-KELM model converged faster than BFO, but the fitness value was less than BFO. The GA-KELM model obtained the maximum number of iteration times for the optimal solution, and the obtained solution was the lowest in the four methods, mainly because the GA algorithm in the current data has poor searching ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{_p_**-Value**} \\ \cline{2-5}  & **ACC** & **MCC** & **Sensitivity** & **Specificity** \\ \hline BFO-KELM & 0.03 & 0.03 & 0.24 & 0.06 \\ PSO-KELM & 0.02 & 0.02 & 0.41 & 0.02 \\ GA-KELM & 0.03 & 0.04 & 0.61 & 0.08 \\ Grid-KELM & 0.02 & 0.02 & 0.23 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(p\)-values of the Wilcoxon test of IBFO-KELM results versus other four methods (\(p>0.05\) are shown in bold). Figure 4: Relationship between the iteration and training accuracy of improved bacterial foraging optimization based KELM (IBFO-KELM), bacterial foraging optimization (BFO-KELM), particle swarm optimization based KELM (PSO-KELM) and genetic algorithm (GA-KELM). In order to show the effectiveness of the proposed method, we also carried out the experiment using the other classifiers including support vector machines (SVM), random forest (RF) and Naive Bayes (NB) for comparison. The grid search method was also used to search for the two values for the radial basis function (RBF) kernel of SVM. The number of trees (ntree) and variables (mtry) were chosen from the range of ntree \(\in\) [50, 500] and mtry \(\in\) [1, 6] with a step of one, and the optimal results was taken for comparison. The detailed results of the above three methods are shown in Table 7. We found that RF achieved the best results among the three methods, while NB performed the worst. SVM ranked between NB and RF. Comparing Tables 5 and 7, we find that KELM can achieve better results than SVM and that the proposed IBFO-KELM can achieve much better results than all the other methods. ## 6 Conclusions and Future Work

In this study, a kind of kernel learning machine model (IBPO-KELM) based on an oppositional bacterial foraging optimization algorithm is proposed to predict the severity of somatization disorder in community correction personnel. The main innovation point of this article is the improved bacterial foraging optimization algorithm (IBFO) proposed on the basis of opposition-based learning strategy. Compared with the original BFO algorithm, this method can not only obtain a solution of the higher quality, but also with a faster convergence speed. Based on this method, KELM can obtain better parameters and higher prediction performance. The experimental results show that the IBFO-KELM model has better performance in terms of ACC, MCC, sensitivity and specificity than the other four KELM models. Therefore, we can certainly come to the conclusion that the proposed intelligent prediction model can well predict the severity of somatization disorder in community correction personnel and provide psychological workers or doctors with meaningful information for clinical decisions and reference. In future work, we will continue to improve the methodology presented in this study, enabling it to be used to identify key factors that affect the physical impairment of community correctional personnel. In addition, we consider collecting more data samples, further improving the performance of the whole model, and introducing the proposed models to other similar mental disease diagnoses.