Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.

Research on the applicability of an exercise rehabilitation app aiming to improve the mental and physical health of breast cancer patients in the post-operative period.

Purpose: Breast cancer is one of the most common malignant cancers in women, seriously endangering the physical and mental health of patients. In this study, we developed an app for breast cancer patients undergoing radiotherapy or chemotherapy with a focus on exercise interventions, supplemented by nutritional and psychological interventions, to verify the applicability of the app for these patients and its impact on their quality of life, sleep, and psychological state. We also investigated the patients' experience and perceptions of the app. Methods: A total of 17 participants, aged 42-58 years, were recruited for this study using a mixed-methods design, including quantitative group pre-and post-test scores and qualitative interview results. The participants used the app for 8-18 weeks depending on their radiotherapy or chemotherapy cycle. During the radiotherapy or chemotherapy period, the participants used the "Yun Dong Ru Kang" exercise rehabilitation app to perform aerobic exercises twice a week, as well as rehabilitation exercises appropriate to their radiotherapy or chemotherapy stage, and used the app on their own the rest of the time. The primary results included their scores on the PSSUQ overall assessment usability questionnaire, the users' use of the app, and the results of the interviews; the secondary indicators were quality of life, sleep status, and anxiety and depression status. Results: An overall score of 6.2 (out of 7 points) on the PSSUQ questionnaire indicates the high usability; the average use time per subject per week was 97.69 ± 11.82 min, which exceeds the minimum use time, but the average use time tended to decrease as the use time was postponed. Promoted articles on nutritional diets received the most hits. The results of the interviews were consistent with the questionnaire scores, with the majority of participants believing that the means of exercise should be enriched and the interface optimized, while the reduction in the length of use was related to the participants' own state of learning about calisthenics. In the results of the Breast Cancer-Specific Scale FACT-B, there was a significant increase (p < 0.05) in the Emotional Status dimension score and a significant decrease (p < 0.05) on the Additional Concerns dimension score. In the results of the Pittsburgh Sleep Quality Inventory PSQI, there was a non-significant improvement in all items except for a significant increase (p < 0.05) for the Hypnotic Medication item. In the Hospital Anxiety and Depression Scale (HADS), there was no significant improvement in any of the anxiety and depression factors. Conclusions: The "Yun Dong Ru Kang "app has certain applicability, and the use of the exercise rehabilitation app may effectively reduce the negative impact of chemotherapy side effects on the quality of life, sleep and depression of breast cancer patients in the chemotherapy or radiotherapy phase. Before it is put into use in the future, the app should be enriched with exercise tools, the interface should be optimized, and articles on nutrition and diet should be promoted.

Emotional Contagion in Physical-Cyber Integrated Networks: The Phase Transition Perspective.

Understanding the emotional contagion process in the crowd will help to take measures in advance to avoid the large-scale spread of negative emotions in emergencies and reduce the loss of lives and properties. Studying the phase transition phenomenon is fundamental to analyzing and evaluating the crowd emotional contagion. However, it is a challenging issue since most people participate in both the physical and cyber networks at the same time. In this article, we focus on the emotional contagion in physical-cyber integrated networks from the phase transition perspective. To achieve this, we first construct a physical-cyber integrated network model to describe the interactions between physical and cyber networks. Second, we build an emotional contagion model to capture the characteristics of emotional contagion in the physical and cyber integrated networks accurately. Finally, we study the phase transition phenomenon of emotional contagion and identify the critical threshold by mapping the emotional contagion to the joint site/bond percolation model. Numerical simulations and experiments further support and enrich our conclusions. The proposed method is expected to provide guidance for controlling emotional contagion in emergencies.

A reliable method for colorectal cancer prediction based on feature selection and support vector machine.

Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. In this study, we build an integrated model based on logistic regression (LR) and support vector machine (SVM) to classify the CRC into cancer and normal samples. From various factors, human location, age, gender, BMI, and cancer tumor type, tumor grade, and DNA, of the cancer, we select the most significant factors (p < 0.05) using logistic regression as main features, and with these features, a grid-search SVM model is designed using different kernel types (Linear, radial basis function (RBF), Sigmoid, and Polynomial). The result of the logistic regression indicates that the Firmicutes (AUC 0.918), Bacteroidetes (AUC 0.856), body mass index (BMI) (AUC 0.777), and age (AUC 0.710) and their combined factors (AUC 0.942) are effective for CRC detection. And the best kernel type is RBF, which achieves an accuracy of 90.1% when k = 5, and 91.2% when k = 10. This study provides a new method for colorectal cancer prediction based on independent risky factors. Graphical abstract Flow chart depicting the method adopted in the study. LR (logistic regression) and ROC curve are used to select independent features as input of SVM. SVM kernel selection aims to find the best kernel function for classification by comparing Linear, RBF, Sigmoid, and Polynomial kernel types of SVM, and the result shows the best kernel is RBF. Classification performance of LR + RF, LR + NB, LR + KNN, and LR + ANNs models are compared with LR + SVM. After these steps, the cancer and healthy individuals can be classified, and the best model is selected.