2023_Modelling The Arrival of Medical Tourists in India
| Format: | General Document |
|---|
| _version_ | 1860798182013796352 |
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2024-01-28 |
| format | General Document |
| id | 16320 |
| institution | UniSZA |
| originalfilename | 16320_ad24df24933ab79.pdf |
| person | Nuzhat Fatema |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16320 |
| sourcemedia | Server storage Scanned document |
| spelling | 16320 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16320 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Business and Management English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin SAMBox 3.0.10; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) Copyright©PWB2025 137 2024-01-28 16320_ad24df24933ab79.pdf Nuzhat Fatema Arrival Of Medical Tourists India Medical tourism Health services accessibility Tourism—Economic aspects Hospitals—India Health services administration 2023_Modelling The Arrival of Medical Tourists in India Introduction: Medical tourism is a vital sector that promotes economic growth of country. Therefore, every country tries to enhance its medical tourism market. India is also one of them and trying to improve medical tourism so that it can attract more medical tourist arrival (MTA). There are several factors, which are dependent on MTA such as advanced medical services with better quality of support services for example visa process, airport facilities services, proper transfer, entertainment and safety which directly affect MTA forecasting. To facilitate and manage such types of services in a proper manner, the Government is required to know in advance an expected number of MTA. While forecasting of MTA is highly dependent on demographic variables such as personal data (PD) of the tourist, age group, sex, level of income, marital status, education, occupation and duration of visit. Moreover, the collection of accurate and reliable primary data based on demographic variables is a challenging task and timeconsuming. The utilization of tourist’s PD is again a challenging task as per the PD law of the country. This motivates to develop an alternative approach using a secondary dataset to forecast the MTA number in advance without using demographic variables and PD information of the tourist, which is a difficult task. Methodology: In this study, a data-driven intelligent approach has been developed for MTA forecasting, which can help business managers and forecasters not only generate reports easily, but also better understand predictions and easier to make strategic decisions based on these predictions. The proposed data-driven intelligent approach is a hybrid approach combining EMD (Empirical Mode Decomposition), ARIMA (Autoregressive Integrated Moving Average) and Monte Carlo (MCS) for multi-step ahead (MSA) medical tourism forecasting. In this study, EMD is used to extract the variables for ARIMA-MCS based forecasting approach. The model’s performance is analyzed using the real site’s secondary dataset, collected from the Ministry of Tourism, India and demonstrated using different case studies to validate the acceptability of the proposed approach. Result: The result evaluation based on proposed approach is demonstrated in two categories. In category 1, the performance analysis is evaluated with the help of collected MTA dataset of 180 months. Whereas in category 2, the performance analysis is evaluated with the help of extracted features from raw-MTA dataset. The forecasting error with EMD and without EMD is in the range of 9.07% - 25.07% and 9.87% - 32.26% respectively, which is in acceptable range for the MTA forecast application. Conclusion: In this study, a novel hybrid intelligent approach for MSA medical tourism forecasting based on EMD, and ARIMA-MCS models is proposed and demonstrated. The presented hybrid model unifies the advantages of EMD, ARIMA and MCS methods, which are capable of learning linear as well as nonlinear behavior of the data and system. The finding of this forecast framework study provides clear, real-time visualization of business performance, which facilitates fast analysis and streamlined business planning for government and private health industry. Dissertations, Academic Thesis |
| spellingShingle | 2023_Modelling The Arrival of Medical Tourists in India |
| state | Terengganu |
| subject | Medical tourism Health services accessibility Tourism—Economic aspects Hospitals—India Health services administration Dissertations, Academic |
| summary | Introduction: Medical tourism is a vital sector that promotes economic growth of country. Therefore, every country tries to enhance its medical tourism market. India is also one of them and trying to improve medical tourism so that it can attract more medical tourist arrival (MTA). There are several factors, which are dependent on MTA such as advanced medical services with better quality of support services for example visa process, airport facilities services, proper transfer, entertainment and safety which directly affect MTA forecasting. To facilitate and manage such types of services in a proper manner, the Government is required to know in advance an expected number of MTA. While forecasting of MTA is highly dependent on demographic variables such as personal data (PD) of the tourist, age group, sex, level of income, marital status, education, occupation and duration of visit. Moreover, the collection of accurate and reliable primary data based on demographic variables is a challenging task and timeconsuming. The utilization of tourist’s PD is again a challenging task as per the PD law of the country. This motivates to develop an alternative approach using a secondary dataset to forecast the MTA number in advance without using demographic variables and PD information of the tourist, which is a difficult task. Methodology: In this study, a data-driven intelligent approach has been developed for MTA forecasting, which can help business managers and forecasters not only generate reports easily, but also better understand predictions and easier to make strategic decisions based on these predictions. The proposed data-driven intelligent approach is a hybrid approach combining EMD (Empirical Mode Decomposition), ARIMA (Autoregressive Integrated Moving Average) and Monte Carlo (MCS) for multi-step ahead (MSA) medical tourism forecasting. In this study, EMD is used to extract the variables for ARIMA-MCS based forecasting approach. The model’s performance is analyzed using the real site’s secondary dataset, collected from the Ministry of Tourism, India and demonstrated using different case studies to validate the acceptability of the proposed approach. Result: The result evaluation based on proposed approach is demonstrated in two categories. In category 1, the performance analysis is evaluated with the help of collected MTA dataset of 180 months. Whereas in category 2, the performance analysis is evaluated with the help of extracted features from raw-MTA dataset. The forecasting error with EMD and without EMD is in the range of 9.07% - 25.07% and 9.87% - 32.26% respectively, which is in acceptable range for the MTA forecast application. Conclusion: In this study, a novel hybrid intelligent approach for MSA medical tourism forecasting based on EMD, and ARIMA-MCS models is proposed and demonstrated. The presented hybrid model unifies the advantages of EMD, ARIMA and MCS methods, which are capable of learning linear as well as nonlinear behavior of the data and system. The finding of this forecast framework study provides clear, real-time visualization of business performance, which facilitates fast analysis and streamlined business planning for government and private health industry. |
| title | 2023_Modelling The Arrival of Medical Tourists in India |
| title_full | 2023_Modelling The Arrival of Medical Tourists in India |
| title_fullStr | 2023_Modelling The Arrival of Medical Tourists in India |
| title_full_unstemmed | 2023_Modelling The Arrival of Medical Tourists in India |
| title_short | 2023_Modelling The Arrival of Medical Tourists in India |
| title_sort | 2023_modelling the arrival of medical tourists in india |