Artificial Intelligence in Medicine 61 (2014) 97–103 Contents lists available at ScienceDirect Artificial Intelligence in Medicine j o ur na l ho mepage: www.elsevier.com/locate/aiim Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals Tung-Kuan Liu a , Yeh-Peng Chen a , Zone-Yuan Hou b , Chao-Chih Wang b , Jyh-Horng Chou a,c,d,∗ a Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan, ROC b Chien Ho Group Practice Clinic, Kaohsiung, No. 278, Qingnian 1st Road, Xinxing District, Kaohsiung 800, Taiwan, ROC c Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, Kaohsiung 807, Taiwan, ROC d Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan, ROC a r t i c l e i n f o Article history: Received 25 December 2011 Received in revised form 18 April 2014 Accepted 5 May 2014 Keywords: Rough set theory Hybrid Taguchi-genetic algorithm Mental stress Stress diagnosis Stress evaluation a b s t r a c t Objective: Evaluating and treating of stress can substantially benefits to people with health problems. Currently, mental stress evaluated using medical questionnaires. However, the accuracy of this evaluation method is questionable because of variations caused by factors such as cultural differences and individual subjectivity. Measuring of biomedical signals is an effective method for estimating mental stress that enables this problem to be overcome. However, the relationship between the levels of mental stress and biomedical signals remain poorly understood. Methods and materials: A refined rough set algorithm is proposed to determine the relationship between mental stress and biomedical signals, this algorithm combines rough set theory with a hybrid Taguchi- genetic algorithm, called RS-HTGA. Two parameters were used for evaluating the performance of the proposed RS-HTGA method. A dataset obtained from a practice clinic comprising 362 cases (196 male, 166 female) was adopted to evaluate the performance of the proposed approach. Results: The empirical results indicate that the proposed method can achieve acceptable accuracy in medical practice. Furthermore, the proposed method was successfully used to identify the relationship between mental stress levels and bio-medical signals. In addition, the comparison between the RS-HTGA and a support vector machine (SVM) method indicated that both methods yield good results. The total averages for sensitivity, specificity, and precision were greater than 96%, the results indicated that both algorithms produced highly accurate results, but a substantial difference in discrimination existed among people with Phase 0 stress. The SVM algorithm shows 89% and the RS-HTGA shows 96%. Therefore, the RS-HTGA is superior to the SVM algorithm. The kappa test results for both algorithms were greater than 0.936, indicating high accuracy and consistency. The area under receiver operating characteristic curve for both the RS-HTGA and a SVM method were greater than 0.77, indicating a good discrimination capability. Conclusions: In this study, crucial attributes in stress evaluation were successfully recognized using biomedical signals, thereby enabling the conservation of medical resources and elucidating the map- ping relationship between levels of mental stress and candidate attributes. In addition, we developed a prototype system for mental stress evaluation that can be used to provide benefits in medical practice. © 2014 Published by Elsevier B.V. 1. Introduction An increasing body of evidence supports the hypothesis that stress plays a critical role in chronic disease; specifically, numerous ∗ Corresponding author at: Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan, ROC. Tel.: +886 7 6011000; fax: +886 7 6011066. E-mail addresses: choujh@kuas.edu.tw, choujh@nkfust.edu.tw (J.-H. Chou). studies have proved that stress produces psychological, physical, and behavioral effects. In addition, previous studies have identified a growing number of chronic ailments associated with stress, such as headache, high blood pressure, and cardiovascular disease, and have indicated that the growth of stress in modern society increases the risk of developing such conditions [1,2]. Therefore, evaluating and treating of stress can lead to improvements in health. Biomedical signals have been proved to be useful in treating var- ious physical and psychological problems. However, several aspects of using this technique are complex. For example, evaluating and http://dx.doi.org/10.1016/j.artmed.2014.05.001 0933-3657/© 2014 Published by Elsevier B.V.