- Volume 10 Issue 8
DOI QR Code
Implementation of Fatigue Identification System using C4.5 Algorithm
C4.5 알고리즘을 이용한 피로도 식별 시스템 구현
- Jin, You Zhen (Dept. of Information and Communication, Far East University) ;
- Lee, Deok-Jin (Dept. of Aviation and IT Convergence, Far East University)
- Received : 2019.05.31
- Accepted : 2019.08.20
- Published : 2019.08.28
This paper proposes a fatigue recognition method using the C4.5 algorithm. Based on domestic and international studies on fatigue evaluation, we have completed the fatigue self - assessment scale in combination with lifestyle and cultural characteristics of Chinese people. The scales used in the text were applied to 58 sub items and were used to assess the type and extent of fatigue. These items fall into four categories that measure physical fatigue, mental fatigue, personal habits, and fatigue outcomes. The purpose of this study is to analyze the leading causes of fatigue formation and to recognize the degree of fatigue, thereby increasing the personal interest in fatigue and reducing the risk of cerebrovascular disease due to excessive fatigue. The recognition rate of the fatigue recognition system using the C4.5 algorithm was 85% on average, confirming the usefulness of this proposal.
Decision tree;C4.5 algorithm;Recognition system;Degree of fatigue;Pattern recognition
- F. B. Liu & J. J. Li. (2011). Research on Suicide and Related Legislation in Japan. Journal of Yunnan University Law Edition, 24(3), 124-129.
- C. X. Luo. (2005). On the Legal Nature of the Overworked Death of Intellectuals and the Perfection of Labor Law. Journal of Agents, (1), 70-73.
- Y. Meng & L. Luo. (1992). Japan's overworked death. World Knowledge, 16, 29.
- T. Z. Shangyu, X. C. Liu & X. L. Ma. (1992). Background of the so-called overwork death. Introduction to Japanese Medicine, 13(1), 5-6.
- M. Kivimaki, G. Batty & M. Hamer. (2011). Using Additional information on working hours to predict coronary heart disease: a cohort study. Ann Intern Med, 154(7), 457-63. https://doi.org/10.7326/0003-4819-154-7-201104050-00003
- T. W. Jang, H. R. Kim, H. E. Lee & J. P. Myong & J. W. Koo. (2013). Long Work hours and obesity in Korean adult workers. Journal of Occupational Health, 55, 359-366. https://doi.org/10.1539/joh.13-0043-OA
- T. Amagasa & T. Nakayama. (2013). Relationship between long working hours and depression: a 3-year longitudinal study of clerical workers. Journal of OCCUP Environ Med, 55(8), 863-872. https://doi.org/10.1097/JOM.0b013e31829b27fa
- S. Viviers et al. (2008). Burnout, psychological distress, and overwork: the case of Quebec's ophthalmologists. Can J Ophthalmol, 43(5), 535-546. DOI : 10.3129/i08-132 https://doi.org/10.3129/i08-132
- A. Wirtz, D. A. Lombardi, J. L. Willetts, S. Folkard & D. C. Christiani. (2012). Gender differences in the effect of weekly working hours on occupational injury risk in the United States working population. J Work Environ Health, 38(4), 349-357. DOI : 10.5271/sjweh.3295 https://doi.org/10.5271/sjweh.3295
- M. Y. Kang et al. (2012). Long working hours and cardiovascular disease. Journal of OCCUP Environ Med, 54(5), 532-537. https://doi.org/10.1097/JOM.0b013e31824fe192
- T. W. Jang et al. (2014). Overwork and cerebrocardiovascular disease in Korean adult workers. Journal of occupational health, 57(1), 51-57. DOI : 10.1539/joh.14-0086-OA.
- T. Devashsish, M. Nisarga & R. D. Sharan. (2010). Re optimization of ID3 and C4.5 decision tree. 2010 International Conference on Computer and Communication Technology(ICCCT). DOI : 10.1109/iccct.2010.5640492
- Y. Zhang & L. Gong. (2004). Principle and technology of data mining, Beijing: Publishing House of Electronics Industry Press.
- H. Byeon. (2015). The Factors of Participating in a Smoking Cessation Program using Integrated Method of Decision Tree and Neural Network Algorithm. Journal of the Korea Convergence Society, 6(2), 25-30.
- S. Y. Oh. (2013). Decision Tree Learning Algorithms for Learning Model Classification in the Vocabulary Recognition System. Journal of Digital Convergence, 11(9), 153-158.
- J. K. Lee & H. W. Lee. (2018). Meltdown Threat Dynamic Detection Mechanism using Decision-Tree based Machine Learning Method. Journal of Convergence for Information Technology, 8(6), 209-215.
- D. M Li, Li Yan, C. Yuan, C. R. Li & H. Liu. (2016). The application of decision tree C4.5 algorithm to soil quality grade forecasting model. 2016 First IEEE International Conference on Computer Communication and the Internet(ICCCI), DOI : 10.1109/cci.2016.7778985
- D. John, J. Cai & Z. Cai. (2005). Decision tree technique and its current research, Control Engineering of China, 12(1), 15-18.