A Machine Learning Approach to Analyze Manpower Sleep Disorder
Archives of Advances in Biosciences,
Vol. 15 No. 1 (2024),
24 Bahman 2024
,
Page 1-11
https://doi.org/10.22037/aab.v15i1.44853
Abstract
Introduction: Human resources play a pivotal role in determining the efficiency of a workplace and an organization. One major issue that significantly influences workforce productivity is sleep disorders. Machine learning can be applied to predict sleep disorders and analyze how various factors, such as lifestyle and environmental conditions, contribute to the development of these disorders, paving the way for more effective interventions and solutions.
Materials and Methods: In this research, by utilizing data analytic methods, some physical and medical-related features of manpower are investigated to make beneficial observations. Moreover, a combination of machine learning and metaheuristic algorithms such as eXtreme Gradient Boosting and particle swarm optimization are used to make an accurate predictive model. Also, the accuracy, recall, precision, and F1-score metrics are utilized to evaluate the model. The Python and Scikit-learn package are used to analyze the problem and implement algorithms.
Results: The outcome is a predictive model with 93.1% accuracy to predict the type of sleep disorder and some useful insights like the relationship of different variables like job and physical characteristics with the sleep disorder. It is observed that one’s occupation has the most impact on insomnia (1.25) and BMI has the most effect on sleep apnea (1).
Conclusion: The implementation of a predictive model helps identify existing issues and enables proactive measures to prevent potential problems, allowing decision-makers to design targeted interventions and wellness programs. Continuous monitoring and adjustments based on the model’s predictions ensure adaptive strategies that improve employee health and workplace efficiency, fostering a resilient workforce and enhancing overall organizational performance.
- Efficiency
- Machine learning
- Metaheuristic
- Manpower
- Sleep disorder
How to Cite
References
Rosekind MR, Gregory KB, Mallis MM, Brandt SL, Seal B, Lerner D. The cost of poor sleep: workplace productivity loss and associated costs. J Occup Environ Med. 2010;52(1):91-8. [DOI: 10.1097/JOM.0b013e3181c78c30]
Ricci JA, Chee E, Lorandeau AL, Berger J. Fatigue in the US workforce: prevalence and
implications for lost productive work time. J Occup Environ Med. 2007;49(1):1. [DOI: 10.1097/01.jom.0000249782.60321.2a]
Chigozie MP, AGA CC, Onyia E. Effect of human capital development in organizational performance in manufacturing industries in South-East Nigeria. International Journal of Academic Research in Economics and Management Sciences. 2018;7(3):60-78. [DOI: DOI: 10.6007/IJAREMS/v7-i3/4378]
Katou AA. How does human resource management influence organisational performance? An integrative approach-based analysis. International Journal of Productivity and Performance Management. 2017;66(6):797-821. [Link]
Johnson RD, Lukaszewski KM, Stone DL. The evolution of the field of human resource information systems: Co-evolution of technology and HR processes. Communications of the Association for Information Systems. 2016;38(1):28. [DOI: 10.17705/1CAIS.03828]
Chang WJ, Liao SH, Wu TT. Relationships among organizational culture, knowledge sharing, and innovation capability: a case of the automobile industry in Taiwan. Knowledge Management Research & Practice. 2017;15(3):471-90. [DOI: 10.1057/s41275-016-0042-6]
Peres RS, Jia X, Lee J, Sun K, Colombo AW, Barata J. Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE access. 2020;8:220121-39. [DOI: 10.1109/ACCESS.2020.3042874]
Amani MA, Sarkodie SA, Sheu JB, Nasiri MM, R Tavakkoli-Moghaddam R. A hybrid scenario-based robust model to design a relief logistics network: a data-driven approach.2023. [DOI:10.2139/ssrn.4377160]
Çınar ZM, Abdussalam Nuhu A, Zeeshan Q, Korhan O, Asmael M, Safaei B. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability. 2020;12(19):8211. [DOI: 10.3390/su12198211]
Javaid M, Haleem A, Singh RP, Suman R. Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management. 2022;7(1):83-111. [DOI: 10.1142/S2424862221300040]
Adel A. Unlocking the future: fostering human–machine collaboration and driving intelligent automation through industry 5.0 in smart cities. Smart Cities. 2023;6(5):2742-82. [DOI: 10.3390/smartcities6050124]
Park S, Zhunis A, Constantinides M, Aiello LM, Quercia D, Cha M. Social dimensions impact individual sleep quantity and quality. Sci Rep. 2023;13(1):9681. [DOI:10.1038/s41598-023-36762-5]
Wang Q, Chu H, Qu P, Fang H, Liang D, Liu S, Li J, Liu A. Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic. Front Nutr. 2023;10:1019827. [DOI: 10.3389/fnut.2023.1019827]
Li S, Sznajder KK, Ning L, Gao H, Xie X, Liu S, Shao C, Li X, Yang X. Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross‐Sectional Study. J Nurs Manag. 2023;2023(1):5524561. [DOI: 10.1155/2023/5524561]
Sharma M, Tiwari J, Patel V, Acharya UR. Automated identification of sleep disorder types using triplet half-band filter and ensemble machine learning techniques with eeg signals. Electronics. 2021;10(13):1531. [DOI: 10.3390/electronics10131531]
Alazaidah R, Samara G, Aljaidi M, Haj Qasem M, Alsarhan A, Alshammari M. Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models. Diagnostics. 2023;14(1):27. [DOI: 10.3390/diagnostics14010027]
Huang AA, Huang SY. Use of machine learning to identify risk factors for insomnia. PloS one. 2023;18(4):e0282622. [DOI: 10.1371/journal.pone.0282622]
Mencar C, Gallo C, Mantero M, Tarsia P, Carpagnano GE, Foschino Barbaro MP, Lacedonia D. Application of machine learning to predict obstructive sleep apnea syndrome severity. Health informatics journal. 2020;26(1):298-317. [DOI: 10.1177/1460458218824725]
Cai L, Datta R, Huang J, Dong S, Du M. Sleep disorder data stream classification based on classifiers ensemble and active learning. In2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2019: 1432-35. [DOI: 10.1109/BIBM47256.2019.8983119]
Widasari ER, Tanno K, Tamura H. Automatic sleep disorders classification using ensemble of bagged tree based on sleep quality features. Electronics. 2020;9(3):512. [DOI: 10.3390/electronics9030512]
Cheng YH, Lech M, Wilkinson RH. Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning. Sensors. 2023;23(7):3468. [DOI: 10.3390/s23073468]
Amani MA, Sarkodie SA. Mitigating spread of contamination in meat supply chain management using deep learning. Sci Rep. 2022;12(1):5037. [DOI: 10.1038/s41598-022-08993-5]
Amani MA, Aghamohammadi N. A novel technology to monitor effects of ethylene on the food products’ supply chain: a deep learning approach. Int J Environ Sci Technol. 2024;21(5):5007-18. [DOI: 10.1007/s13762-023-05328-3]
Nayak J, Swapnarekha H, Naik B, Dhiman G, Vimal S. 25 years of particle swarm optimization: Flourishing voyage of two decades. Arch Computat Methods Eng. 2023;30(3):1663-725. [DOI: 10.1007/s11831-022-09849-x]
Du B, Wei Q, Liu R. An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Transactions on Geoscience and Remote Sensing. 2019;57(8):6003-17. [DOI: 10.1109/TGRS.2019.2903875]
Piotrowski AP, Napiorkowski JJ, Piotrowska AE. Particle swarm optimization or differential evolution—A comparison. Engineering Applications of Artificial Intelligence. 2023;121:106008. [DOI: 10.1016/j.engappai.2023.106008]
Yang X, Li H. Evolutionary-state-driven multi-swarm cooperation particle swarm optimization for complex optimization problem. Information Sciences. 2023;646:119302. [DOI:10.1016/j.ins.2023.119302]
Amani MA, Nasiri MM. A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach. J Comb Optim. 2023;45(5):130. [DOI: 10.1007/s10878-023-01057-y]
Kiangala SK, Wang Z. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment. Mach Learn Appl. 2021;4:100024. [DOI: 10.1016/j.mlwa.2021.100024]
Asselman A, Khaldi M, Aammou S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments. 2023;31(6):3360-79. [DOI: 10.1080/10494820.2021.1928235]
Dhaliwal SS, Nahid AA, Abbas R. Effective intrusion detection system using XGBoost. Information. 2018;9(7):149. [DOI: 10.3390/info9070149]
Wang Y, Pan Z, Zheng J, Qian L, Li M. A hybrid ensemble method for pulsar candidate classification. Astrophys Space Sci. 2019;364:1-3. [DOI: 10.1007/s10509-019-3602-4]
Deng X, Liu Q, Deng Y, Mahadevan S. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences. 2016;340:250-61. [DOI:10.1016/j.ins.2016.01.033]
Talukder MA, Layek MA, Kazi M, Uddin MA, Aryal S. Empowering covid-19 detection: Optimizing performance through fine-tuned efficientnet deep learning architecture. Computers in Biology and Medicine. 2024;168:107789. [DOI:10.1016/j.compbiomed.2023.107789]
Dahouda MK, Joe I. A deep-learned embedding technique for categorical features encoding. IEEE Access. 2021;9:114381-91. [DOI: 10.1109/ACCESS.2021.3104357]
Zhao W, Kong S, Bai J, Fink D, Gomes C. Hot-vae: Learning high-order label correlation for multi-label classification via attention-based variational autoencoders. InProceedings of the AAAI conference on artificial intelligence 2021; 35(17):15016-24. [DOI: 10.1609/aaai.v35i17.17762]
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