Prediction of Sleep Disorders Based on Occupation and Lifestyle: Performance Comparison of Decision Tree, Random Forest, and Naïve Bayes Classifier

Authors

  • Heru Lestiawan University of Dian Nuswantoro
  • Cahaya Jatmoko University of Dian Nuswantoro
  • Feri Agustina University of Dian Nuswantoro
  • Daurat Sinaga University of Dian Nuswantoro
  • Lalang Erawan University of Dian Nuswantoro

DOI:

https://doi.org/10.33633/jais.v8i3.8987

Abstract

Health is a very important thing in life. Therefore, to maintain health, we need adequate rest. Without adequate rest, the body will not be healthy and fit. In this study, a person's sleep disorder prediction will be made based on their lifestyle and work. The predictions made will classify sleep disorders that are absent, sleep apnea and insomnia from certain lifestyles and work. The methods used to make predictions are decision tree classifier, random forest classifier and naïve Bayes classifier. The test was carried out using a total of 375 data which was broken down into 70% training data and 30% testing data. The results obtained after testing with test data are by using the decision tree classifier algorithm to get an accuracy of 89.431%, using the random forest classifier algorithm to get an accuracy of 90.244% and by using the naïve Bayes classifier algorithm to get an accuracy of 86.992%.

References

Engelen, L. (2020). Does active design influence activity, sitting, wellbeing and productivity in the workplace? A systematic review. In International Journal of Environmental Research and Public Health (Vol. 17, Issue 24, pp. 1–15). MDPI AG. https://doi.org/10.3390/ijerph17249228

Lukwa, A. T., Mawoyo, R., Zablon, K. N., Siya, A., & Alaba, O. (2019). Effect of malaria on productivity in a workplace: The case of a banana plantation in Zimbabwe. Malaria Journal, 18(1). https://doi.org/10.1186/s12936-019-3021-6

Niati, D. R., Siregar, Z. M. E., & Prayoga, Y. (2021). The Effect of Training on Work Performance and Career Development: The Role of Motivation as Intervening Variable. Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences, 4(2), 2385–2393. https://doi.org/10.33258/birci.v4i2.1940

Yusuf Iis, E., Wahyuddin, W., Thoyib, A., Nur Ilham, R., & Sinta, I. (2022). THE EFFECT OF CAREER DEVELOPMENT AND WORK ENVIRONMENT ON EMPLOYEE PERFORMANCE WITH WORK MOTIVATION AS INTERVENING VARIABLE AT THE OFFICE OF AGRICULTURE AND LIVESTOCK IN ACEH. International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS), 2(2), 227–236. https://doi.org/10.54443/ijebas.v2i2.191

Asaloei, S. I., Wolomasi, A. K., & Werang, B. R. (2020). Work-related stress and performance among primary school teachers. International Journal of Evaluation and Research in Education, 9(2), 352–358. https://doi.org/10.11591/ijere.v9i2.20335

Merga, H., & Fufa, T. (2019). Impacts of working environment and benefits packages on the health professionals’ job satisfaction in selected public health facilities in eastern Ethiopia: Using principal component analysis. BMC Health Services Research, 19(1). https://doi.org/10.1186/s12913-019-4317-5

Pisaniello, M. S., Asahina, A. T., Bacchi, S., Wagner, M., Perry, S. W., Wong, M. L., & Licinio, J. (2019). Effect of medical student debt on mental health, academic performance and specialty choice: A systematic review. In BMJ Open (Vol. 9, Issue 7). BMJ Publishing Group. https://doi.org/10.1136/bmjopen-2019-029980

Nowrouzi-Kia, B., Sithamparanathan, G., Nadesar, N., Gohar, B., & Ott, M. (2022). Factors associated with work performance and mental health of healthcare workers during pandemics: a systematic review and meta-analysis. In Journal of Public Health (United Kingdom) (Vol. 44, Issue 4, pp. 731–739). Oxford University Press. https://doi.org/10.1093/pubmed/fdab173

Magnavita, N. (2022). Headache in the Workplace: Analysis of Factors Influencing Headaches in Terms of Productivity and Health. International Journal of Environmental Research and Public Health, 19(6). https://doi.org/10.3390/ijerph19063712

Mujan, I., An?elkovi?, A. S., Mun?an, V., Kljaji?, M., & Ruži?, D. (2019). Influence of indoor environmental quality on human health and productivity - A review. Journal of Cleaner Production, 217, 646–657. https://doi.org/10.1016/j.jclepro.2019.01.307

Zhao, M., Tuo, H., Wang, S., & Zhao, L. (2020). The Effects of Dietary Nutrition on Sleep and Sleep Disorders. In Mediators of Inflammation (Vol. 2020). Hindawi Limited. https://doi.org/10.1155/2020/3142874

Kansagra, S. (2020). Sleep disorders in adolescents. Pediatrics, 145(2). https://doi.org/10.1542/PEDS.2019-2056I

Streatfeild, J., Smith, J., Mansfield, D., Pezzullo, L., & Hillman, D. (2021). The social and economic cost of sleep disorders. Sleep, 44(11). https://doi.org/10.1093/sleep/zsab132

Huyett, P., & Bhattacharyya, N. (2021). Incremental health care utilization and expenditures for sleep disorders in the United States. Journal of Clinical Sleep Medicine, 17(10), 1981–1986. https://doi.org/10.5664/jcsm.9392

Simonetti, V., Durante, A., Ambrosca, R., Arcadi, P., Graziano, G., Pucciarelli, G., Simeone, S., Vellone, E., Alvaro, R., & Cicolini, G. (2021). Anxiety, sleep disorders and self-efficacy among nurses during COVID-19 pandemic: A large cross-sectional study. Journal of Clinical Nursing, 30(9–10), 1360–1371. https://doi.org/10.1111/jocn.15685

Wang, X., Cheng, S., & Xu, H. (2019). Systematic review and meta-analysis of the relationship between sleep disorders and suicidal behaviour in patients with depression. In BMC Psychiatry (Vol. 19, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s12888-019-2302-5

K. Pavlova, M., & Latreille, V. (2019). Sleep Disorders. In American Journal of Medicine (Vol. 132, Issue 3, pp. 292–299). Elsevier Inc. https://doi.org/10.1016/j.amjmed.2018.09.021

Merrill, R. M. (2022). Mental Health Conditions According to Stress and Sleep Disorders. International Journal of Environmental Research and Public Health, 19(13). https://doi.org/10.3390/ijerph19137957

S. Ray, "A Quick Review of Machine Learning Algorithms," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 35-39, doi: 10.1109/COMITCon.2019.8862451.

Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222–2239. https://doi.org/10.1093/aje/kwz189

Appadoo, K., Soonnoo, M. B., & Mungloo-Dilmohamud, Z. (2020, December 16). Job Recommendation System, Machine Learning, Regression, Classification, Natural Language Processing. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. https://doi.org/10.1109/CSDE50874.2020.9411584

S. Ray, "A Quick Review of Machine Learning Algorithms," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 35-39, doi: 10.1109/COMITCon.2019.8862451.

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. In SN Computer Science (Vol. 2, Issue 3). Springer. https://doi.org/10.1007/s42979-021-00592-x

Kumar, D. (2020). Biographical notes: Priyanka received her Bachelor of Technology in Computer Science and Engineering (CSE) and Master of Technology in CSE from GJUS&T. In Int. J. Information and Decision Sciences (Vol. 12, Issue 3). https://doi.org/10.1504/IJIDS.2020.108141

Yadav, D. C., & Pal, S. (2020). Prediction of thyroid disease using decision tree ensemble method. Human-Intelligent Systems Integration, 2(1–4), 89–95. https://doi.org/10.1007/s42454-020-00006-y

Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K. (2020). BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model. Mobile Networks and Applications, 25(3), 1151–1161. https://doi.org/10.1007/s11036-019-01443-z

Lan, T., Hu, H., Jiang, C., Yang, G., & Zhao, Z. (2020). A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Advances in Space Research, 65(8), 2052–2061. https://doi.org/10.1016/j.asr.2020.01.036

Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. In Expert Systems with Applications (Vol. 134, pp. 93–101). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2019.05.028

Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. Stata Journal, 20(1), 3–29. https://doi.org/10.1177/1536867X20909688

Pal, M., & Parija, S. (2021). Prediction of Heart Diseases using Random Forest. Journal of Physics: Conference Series, 1817(1). https://doi.org/10.1088/1742-6596/1817/1/012009

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192. https://doi.org/10.1016/j.knosys.2019.105361

A. M. Rahat, A. Kahir and A. K. M. Masum, "Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset," 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, 2019, pp. 266-270, doi: 10.1109/SMART46866.2019.9117512.

G. Singh, B. Kumar, L. Gaur and A. Tyagi, "Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification," 2019 International Conference on Automation, Computational and Technology Management (ICACTM), London, UK, 2019, pp. 593-596, doi: 10.1109/ICACTM.2019.8776800.

Dimitriadis, S. I., Salis, C. I., & Liparas, D. (2021). An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model. Journal of Neural Engineering, 18(4). https://doi.org/10.1088/1741-2552/abf773

Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019). A comparison between ARIMA, LSTM, and GRU for time series forecasting. ACM International Conference Proceeding Series, 49–55. https://doi.org/10.1145/3377713.3377722

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Published

2023-11-30