Application of fuzzy genetic system to predict the number of outpatient visits

Authors

  • Sarif Surorejo STMIK YMI Tegal, Indonesia
  • Septian Dwi Cahyo STMIK YMI Tegal, Indonesia
  • Nurul Fadilah STMIK YMI Tegal, Indonesia
  • Gunawan Gunawan STMIK YMI Tegal, Indonesia

DOI:

https://doi.org/10.35335/mandiri.v13i1.299

Keywords:

Fuzzy System, Genetic Algorithm, Outpatient, Patient Visits, Predictions

Abstract

Improving the management and use of resources in outpatient care is a challenge faced by health facilities in today's digital era. The inability to accurately predict patient flow can result in inadequacies in staff scheduling and effective space management. Therefore, this study aims to develop a predictive model of outpatient visits using the fuzzy system genetic method. The research methods used include the design of a combination of experimental methods, quantitative analysis, and model validation. Outpatient visit data is taken from a hospital and processed using the Fuzzy Genetics System which optimizes fuzzy rules with genetic algorithms. The results of the model implementation show accurate and adaptive predictions to variations and uncertainties in patient visiting patterns. Based on the results of the study, it can be concluded that the use of fuzzy system genetic methods in predicting outpatient visits can improve the operational efficiency of health facilities. The developed prediction model is able to provide predictions that are more accurate, adaptive, and responsive to the real needs of health facilities. With the adoption of this method, health facilities can optimize management and resources in outpatient health services. This research contributes significantly to the development of predictive models that are more efficient and applicable in the dynamic context of healthcare.

References

Alidina, S., Martelli, P. F., Singer, S. J., & Aveling, E. L. (2021). Optimizing patient partnership in primary care improvement: A qualitative study. Health Care Management Review, 46(2), 123–134. https://doi.org/10.1097/HMR.0000000000000250

Bhandari, S., Tak, A., Singhal, S., Shukla, J., Shaktawat, A. S., Gupta, J., Patel, B., Kakkar, S., Dube, A., Dia, S., Dia, M., & Wehner, T. C. (2020). Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.585850

Decerle, J., Grunder, O., El Hassani, A. H., & Barakat, O. (2019). A memetic algorithm for multi-objective optimization of the home health care problem. Swarm and Evolutionary Computation, 44, 712–727. https://doi.org/10.1016/j.swevo.2018.08.014

Deng, Y., Fan, H., & Wu, S. (2023). A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits. Journal of Ambient Intelligence and Humanized Computing, 1–11. https://doi.org/10.1007/s12652-020-02602-x

Garud, K. S., Jayaraj, S., & Lee, M. (2021). A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models. International Journal of Energy Research, 45(1), 6–35. https://doi.org/10.1002/er.5608

Halawa, F., Madathil, S. C., Gittler, A., & Khasawneh, M. T. (2020). Advancing evidence-based healthcare facility design: a systematic literature review. Health Care Management Science, 23, 453–480. https://doi.org/10.1007s10729-020-09506-4

Hamdia, K. M., Zhuang, X., & Rabczuk, T. (2021). An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Computing and Applications, 33(6), 1923–1933. https://doi.org/10.1007/s00521-020-05035-x

Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S., & Jo, O. (2020). COVID-19 patient health prediction using boosted random forest algorithm. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.00357

Khan, P. W., & Byun, Y. C. (2020). Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction. IEEE Access, 8, 196274–196286. https://doi.org/10.1109/ACCESS.2020.3034101

Król, A., & Sierpiński, G. (2021). Application of a genetic algorithm with a fuzzy objective function for optimized siting of electric vehicle charging devices in urban road networks. IEEE Transactions on Intelligent Transportation Systems, 23(7), 8680–8691. https://doi.org/10.1109/TITS.2021.3085103

Luo, P., Sun, Y., Wang, S., Wang, S., Lyu, J., Zhou, M., Nakagami, K., Takara, K., & Nover, D. (2020). Historical assessment and future sustainability challenges of Egyptian water resources management. Journal of Cleaner Production, 263, 121154. https://doi.org/10.1016j.jclepro.2020.121154

Mamun, A. Al, Sohel, M., Mohammad, N., Haque Sunny, M. S., Dipta, D. R., & Hossain, E. (2020). A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models. In IEEE Access (Vol. 8, pp. 134911–134939). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2020.3010702

Mousa, S. K., & Othman, M. (2020). The impact of green human resource management practices on sustainable performance in healthcare organisations: A conceptual framework. Journal of Cleaner Production, 243, 118595. https://doi.org/10.1016/j.jclepro.2019.118595

Munir, K., de Ramón-Fernández, A., Iqbal, S., & Javaid, N. (2019). Neuroscience patient identification using big data and fuzzy logic–An Alzheimer’s disease case study. Expert Systems with Applications, 136, 410–425. https://doi.org/10.1016/j.eswa.2019.06.049

Safdar, S., Ahmed Khan, S., Shaukat, A., & Akram, M. U. (2021). Genetic Algorithm Based Automatic Out-Patient Experience Management System (GAPEM) Using RFIDs and Sensors. IEEE Access, 9, 8961–8976. https://doi.org/10.1109/ACCESS.2020.3046839

Singh, N., Singh, P., & Bhagat, D. (2019). A rule extraction approach from support vector machines for diagnosing hypertension among diabetics. Expert Systems with Applications, 130, 188–205. https://doi.org/10.1016/j.eswa.2019.04.029

Tabbussum, R., & Dar, A. Q. (2021). Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Environmental Science and Pollution Research, 28(20), 25265–25282. https://doi.org/10.1007s11356-021-12410-1

Talpur, N., Abdulkadir, S. J., Alhussian, H., Hasan, · Mohd Hilmi, Aziz, N., & Bamhdi, A. (2022). A comprehensive review of deep neuro-fuzzy system architectures and their optimization methods. Neural Computing and Applications, 1–39. https://doi.org/10.1007/s00521-021-06807-9

Verma, P., Garg, R., & Mahajan, P. (2020). Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition. ISA Transactions, 100, 251–263. https://doi.org/10.1016/j.isatra.2020.01.009

Wang, J. J., Dai, Z., Chang, A. C., & Shi, J. J. (2022). Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations. Annals of Operations Research, 315(1), 463–505. https://doi.org/10.1007/s10479-022-04645-z

Weaver, R. J., Blomme, E. A., Chadwick, A. E., Copple, I. M., Gerets, H. H. J., Goldring, C. E., Guillouzo, A., Hewitt, P. G., Ingelman-Sundberg, M., & Jensen, K. G. (2020). Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models. Nature Reviews Drug Discovery, 19(2), 131–148. https://doi.org/10.1038/s41573-019-0048-x

Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., & Penoza, C. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070. https://doi.org/10.1001/jamainternmed.2021.2626

Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., Bonten, M. M. J., Damen, J. A. A., Debray, T. P. A., De Vos, M., Dhiman, P., Haller, M. C., Harhay, M. O., Henckaerts, L., Kreuzberger, N., Lohmann, A., Luijken, K., Ma, J., Andaur Navarro, C. L., … Van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal. The BMJ, 369. https://doi.org/10.1136/bmj.m1328

Yang, C.-H., Moi, S.-H., Hou, M.-F., Chuang, L.-Y., & Lin, Y.-D. (2020). Applications of deep learning and fuzzy systems to detect cancer mortality in next-generation genomic data. IEEE Transactions on Fuzzy Systems, 29(12), 3833–3844. https://doi.org/10.1109/TFUZZ.2020.3028909

Yu, H., Wang, P., Zheng, H., Luo, J., & Liu, J. (2020). Impacts of congestion on healthcare outcomes: an empirical observation in China. Journal of Management Analytics, 7(3), 344–366. https://doi.org/10.1080/23270012.2020.1731720

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Published

2024-06-13

How to Cite

Surorejo, S., Cahyo, S. D., Fadilah, N., & Gunawan, G. (2024). Application of fuzzy genetic system to predict the number of outpatient visits. Jurnal Mandiri IT, 13(1), 47–55. https://doi.org/10.35335/mandiri.v13i1.299

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