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Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

Firat Kilincer Ilhan, Ertam Fatih, Sengur Abdulkadir, Tan Ru-San, Rajendra Acharya U.

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 1

30--41

Widespread proliferation of interconnected healthcare equipment, accompanying software, operating systems, and networks in the Internet of Medical Things (IoMT) raises the risk of security compromise as the bulk of IoMT devices are not built to withstand internet attacks. In this work, we have developed a cyber-attack and anomaly detection model based on recursive feature elimination (RFE) and multilayer perceptron (MLP). The RFE approach selected optimal features using logistic regression (LR) and extreme gradient boosting regression (XGBRegressor) kernel functions. MLP parameters were adjusted by using a hyperparameter optimization and 10-fold cross-validation approach was performed for performance evaluations. The developed model was performed on various IoMT cybersecurity datasets, and attained the best accuracy rates of 99.99%, 99.94%, 98.12%, and 96.2%, using Edith Cowan University- Internet of Health Things (ECU-IoHT), Intensive Care Unit (ICU Dataset), Telemetry data, Operating systems’ data, and Network data from the testbed IoT/IIoT network (TON-IoT), and Washington University in St. Louis enhanced healthcare monitoring system (WUSTL-EHMS) datasets, respectively. The proposed method has the ability to counter cyber attacks in healthcare applications.

Investigating the effects of local weather, streamflow lag, and global climate information on 1 month ahead streamflow forecasting by using XGBoost and SHAP: two case studies involving the contiguous USA

Liu Jin, Ren Kun, Ming Tingzhen, Qu Jihong, Guo Wenxian, Li Haohao

Acta Geophysica

2023

Vol. 71, no. 2

905--925

The use of machine learning (ML) models for streamflow forecasting has recently proved highly successful. However, ML is typically criticized for a lack of interpretability. Here, we develop an interpretable ML model for 1-month-ahead streamflow forecasting using extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). In addition to a performance evaluation of XGBoost compared to regression tree and random forest approaches, the effects of input variables, including local weather, streamflow lag, and global climate, on streamflow were interpreted in terms of SHAP total effect values, main effect values, interaction values, and loss values. The experimental results at two catchments in the contiguous USA are significant in four ways. First, XGBoost was superior to the other two models in terms of Nash–Sutclife efficiency, mean absolute error, root mean square error, and correlation coefficient. Second, by aggregating SHAP values, we found that the contributions of these variables to streamflow differed according to the investigated local perspectives, including streamflow at different months, low streamflow, medium streamflow, high streamflow, and peak streamflow. Third, the SHAP main effect and interaction values revealed that nonmonotonic relationships may occur between the input variables and streamflow, and the strength of variable interaction effects might be related to the variable values rather than their correlations. Fourth, variable drifts in the testing set were deduced from SHAP loss values. These findings exhibit positive significance for understanding ML for monthly streamflow forecasting.

Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis

Mateo J., Rius-Peris J.M., Maraña-Pérez A.I., Valiente-Armero A., Torres A.M.

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 2

792--801

Acute bronchiolitis is the most common lower respiratory tract infection of infancy. About 2% of infants under 12 months of age hospitalized with this condition each epidemic season. The choice of the correct treatment is important for the evolution of the disease. Therefore, a prediction model for medical treatment identification based on extreme gradient boosting (XGB) machine learning (ML) method is proposed in this paper. Four supervised machine learning algorithms including a k-nearest neighbours (KNN), decision tree (DT), Gaussian Naı¨ve Bayes (GNB) and support vector machine (SVM) were compared with the proposed XGB method. The performance of these methods was then tested implementing a standard 10-fold cross-validation process. The results indicate that the XGB has the best prediction accuracy (94%), high precision (>0.94) and high recall (>0.94). The KNN, SVM, and DT approaches also present moderate prediction accuracy (>87), moderate specificity (>0.87) and moderate sensitivity (>0.87). The GNB algorithm show relatively low classification performance. Based on these results for classification performance and prediction accuracy, the XGB is a solid candidate for a correct classification of patients to be treated. These findings suggest that XGB systems trained with clinical data may serve as a new tool to assist in the treatment of patients with acute bronchiolitis.