Comparative Study of Factors Influencing the Adoption or Non-Adoption of Healthcare Services at Newly Opening Private Hospitals
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Abstract
A comparative study using logistic regression and discriminant analysis examines the importance of both methods in health services. The study compares both methods' data categorization using 2 favorite Statistics to determine their relevance in healthcare. The study focuses on Chiang Rai province in northern Thailand and the factors that influence the selection of newly established private hospitals. The study examines service quality, social insurance entitlements, and patient behavior during illness. The study helps emerging private healthcare facilities understand the complex relationship between service quality and patient response, use statistically sound methods for accurate predictions, and improve health services.
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ผลงานที่ปรากฎในวารสารฉบับนี้เป็นลิขสิทธิ์เฉพาะส่วนบุคคลของผู้เขียนซึ่งต้องรับผิดชอบต่อผลทาง กฎหมายที่อาจเกิดขึ้นได้และไม่มีผลต่อกองบรรณาธิการReferences
Aiello, T. F., Puerta-Alcalde, P., Chumbita, M., Monzó, P., Lopera, C., Hurtado, J. C., ... & Garcia-Vidal, C. (2022). Infection with the Omicron variant of SARS-CoV-2 is associated with less severe disease in hospitalized patients with COVID-19. Journal of Infection, 85(5), e152-e154.
Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical care, 9(1), 1-7.
Boateng, E. Y., & Abaye, D. A. (2019). A review of the logistic regression model with emphasis on medical research. Journal of data analysis and information processing, 7(4), 190-207.
Bujang, M. A., Sa’at, N., Bakar, T. M. I. T. A., & Joo, L. C. (2018). Sample size guidelines for logistic regression from observational studies with large population: emphasis on the accuracy between statistics and parameters based on real life clinical data. The Malaysian journal of medical sciences: MJMS, 25(4), 122.
Cacoullos, T. (Ed.). (2014). Discriminant analysis and applications. Academic Press.
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.
Dhungana, R. R., Pandey, A. R., & Shrestha, N. (2021). Trends in the prevalence, awareness, treatment, and control of hypertension in Nepal between 2000 and 2025: a systematic review and meta-analysis. International journal of hypertension, 2021, 1-11.
Emanet, N., Öz, H. R., Bayram, N., & Delen, D. (2014). A comparative analysis of machine learning methods for classification type decision problems in healthcare. Decision Analytics, 1, 1-20.
Essex, R., Ahmed, S., Elliott, H., Lakika, D., Mackenzie, L., & Weldon, S. M. (2023). The impact of strike action on healthcare delivery: a scoping review. The International Journal of Health Planning and Management, 38(3), 599-627.
Google. (n.d.). [The hospitals in Chaing Rai - Google Maps]. Retrieved October 1, 2023, from https://www.google.com/maps/@19.8852803,99.7534968,12z?entry=ttu
Gupta, M. K., & Chandra, P. (2020). A comprehensive survey of data mining. International Journal of Information Technology, 12(4), 1243-1257.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
Hofmann, M., & Klinkenberg, R. (Eds.). (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Horváthová, J., & Mokrišová, M. (2020). Comparison of the results of a data envelopment analysis model and logit model in assessing business financial health. Information, 11(3), 160.
Jothi, N., & Husain, W. (2015). Data mining in healthcare–a review. Procedia computer science, 72, 306-313.
Kaur, R., & Kaur, G. (2023). Managerial leadership and turnover intentions with the mediating role of employee happiness: an empirical study based on private universities. The TQM Journal, ahead-of-print.
Kiani, J., Camp, C., & Pezeshk, S. (2019). On the application of machine learning techniques to derive seismic fragility curves. Computers & Structures, 218, 108-122.
László, K., & Ghous, H. (2020). Efficiency comparison of Python and RapidMiner. Multidiszciplináris Tudományok, 10(3), 212-220.
Li, P., Luo, Y., Yu, X., Mason, E., Zeng, Z., Wen, J., ... & Jalali, M. S. (2022). Readiness of healthcare providers for e-hospitals: a cross-sectional analysis in China before the COVID-19 period. BMJ open, 12(2), e054169.
Liu, X. (2015). Methods and applications of longitudinal data analysis. Elsevier.
Lock, J. Z., Khoo, Z. X., & Pek, J. H. (2023). Paediatric one-day admission: why and is it necessary?. Singapore Medical Journal.
Mazanec, J., & Bartosova, V. (2021). Prediction model as sustainability tool for assessing financial status of non-profit organizations in the Slovak Republic. Sustainability, 13(17), 9721.
Meechaiyo, N., & Guo, J. (2019). Strategic Development and Comparative Advantages of Mengla Border Special Economic Zone, China and Chiang Rai Special Economic Zone, Thailand. Journal of Mekong Societies, 15(2), 55-70.
Mehrolia, S., Alagarsamy, S., & Solaikutty, V. M. (2021). Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. International journal of consumer studies, 45(3), 396-408.
Mousavi, S. M. H., Agharahimi, Z., Daryabeigi, M., & Rezaei, N. (2014). Implementation of clinical governance in hospitals: challenges and the keys for success. Acta Medica Iranica, 493-495.
Naser, M. Z. (2023). Machine learning for all! Benchmarking automated, explainable, and coding-free platforms on civil and environmental engineering problems. Journal of Infrastructure Intelligence and Resilience, 2(1), 100028.
Nasri, K., Amara, M., & Helmi, I. (2022, September). The Landscape of Social Protection in Tunisia. Economic Research Forum (ERF).
Nikita, E., & Nikitas, P. (2020). Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables. International journal of legal medicine, 134(3), 1213-1225.
O'Connell, A. A. (2006). Logistic regression models for ordinal response variables (Vol. 146). sage.
Pohar, M., Blas, M., & Turk, S. (2004). Comparison of logistic regression and linear discriminant analysis: a simulation study. Metodoloski zvezki, 1(1), 143.
Punpukdee, A. (2023). Considering Consumer Behavior in Thailand's Traditional and Digital Markets. Journal of Contemporary Issues and Thought, 13(1), 25-34.
Punpukdee, A., Wattana, C., Punpairoj, W., Srichuachom, U., Yaklai, P., & Trongtortam, S. (2021). Key Factors Affecting Consumer Behavior in the Thailand Offline Marketing: Research Synthesis by Systematic Literature Review and Data Mining Techniques. Journal of Humanities and Social Sciences Thonburi University, 15(2), 21-32.
Rossi, J. S. (1990). Statistical power of psychological research: What have we gained in 20 years?. Journal of consulting and clinical psychology, 58(5), 646.
Sohrabi, B., Raeesi Vanani, I., Nikaein, N., & Kakavand, S. (2019). A predictive analytics of physicians prescription and pharmacies sales correlation using data mining. International Journal of Pharmaceutical and Healthcare Marketing, 13(3), 346-363.
Stella, O. (2019). Discriminant analysis: An analysis of its predictship function. Journal of Education and Practice, 10(5), 50-57.
Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497-516). Boston, MA: pearson.
Tavassoli, M., & Saen, R. F. (2019). Predicting group membership of sustainable suppliers via data envelopment analysis and discriminant analysis. Sustainable Production and Consumption, 18, 41-52.
Tillmanns, S., & Krafft, M. (2021). Logistic regression and discriminant analysis. In Handbook of market research (pp. 329-367). Cham: Springer International Publishing.
Ushakov, D. S., Yushkevych, O. O., Ovander, N. L., Tkachuk, H. Y., & Vyhovskyi, V. H. (2019). The strategy of Thai medical services promotion at foreign markets and development of medical tourism. Geo Journal of Tourism and Geosites, 27(4), 1429-1438.
Worth, A. P., & Cronin, M. T. (2003). The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. Journal of Molecular Structure: THEOCHEM, 622(1-2), 97-111.