A framework for predicting employee health risks using Ensemble Model
Through the phenomenon of data, big data and data analytics have provided an opportunity to collect, store, process, analyze and visualize an immense amount of information. Healthcare is recognized as one of the most information-intensive sectors. An urge to explore analytics has been sparked by th...
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| Format: | Article |
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Institute of Advanced Science Extension (IASE)
2021
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| Online Access: | http://eprints.sunway.edu.my/1840/ |
| _version_ | 1848802145719025664 |
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| author | Chan, Nicholas Kin Whai Lee, Angela Siew Hoong * Zuraini Zainol, |
| author_facet | Chan, Nicholas Kin Whai Lee, Angela Siew Hoong * Zuraini Zainol, |
| author_sort | Chan, Nicholas Kin Whai |
| building | SU Institutional Repository |
| collection | Online Access |
| description | Through the phenomenon of data, big data and data analytics have provided an opportunity to collect, store, process, analyze and visualize an immense amount of information. Healthcare is recognized as one of the most
information-intensive sectors. An urge to explore analytics has been sparked by the rapid growth of data within the healthcare sector. Most employers in Malaysia provide medical benefits that are included in the medical insurance
plan for their employees. Data collected such as the history of medical claims are stored with the HR (Human Resource) which contributes to the potential of analyzing and recognizing trends within medical claims to better
understand the use and overall health of the employee population. Patients with higher risk will generally convert into patients with high costs. Hence, early intervention of these patients will allow employers to potentially
minimize costs and plan preventative steps. In predictive analysis, Decision Trees and Regression are typical techniques applied. The proposed framework combines an ensemble technique known as Stacking. As opposed
to a single predictive model, an ensemble predictive model would yield better performance and accuracy. The objective of this paper is therefore to review current practices and past research within the healthcare sector while suggesting a practical framework for classification ensemble modeling.
Preliminary findings indicated that an ensemble model can produce higher predictive accuracy and performance than a single model. |
| first_indexed | 2025-11-14T21:18:42Z |
| format | Article |
| id | sunway-1840 |
| institution | Sunway University |
| institution_category | Local University |
| last_indexed | 2025-11-14T21:18:42Z |
| publishDate | 2021 |
| publisher | Institute of Advanced Science Extension (IASE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | sunway-18402021-10-13T03:00:45Z http://eprints.sunway.edu.my/1840/ A framework for predicting employee health risks using Ensemble Model Chan, Nicholas Kin Whai Lee, Angela Siew Hoong * Zuraini Zainol, QA75 Electronic computers. Computer science Through the phenomenon of data, big data and data analytics have provided an opportunity to collect, store, process, analyze and visualize an immense amount of information. Healthcare is recognized as one of the most information-intensive sectors. An urge to explore analytics has been sparked by the rapid growth of data within the healthcare sector. Most employers in Malaysia provide medical benefits that are included in the medical insurance plan for their employees. Data collected such as the history of medical claims are stored with the HR (Human Resource) which contributes to the potential of analyzing and recognizing trends within medical claims to better understand the use and overall health of the employee population. Patients with higher risk will generally convert into patients with high costs. Hence, early intervention of these patients will allow employers to potentially minimize costs and plan preventative steps. In predictive analysis, Decision Trees and Regression are typical techniques applied. The proposed framework combines an ensemble technique known as Stacking. As opposed to a single predictive model, an ensemble predictive model would yield better performance and accuracy. The objective of this paper is therefore to review current practices and past research within the healthcare sector while suggesting a practical framework for classification ensemble modeling. Preliminary findings indicated that an ensemble model can produce higher predictive accuracy and performance than a single model. Institute of Advanced Science Extension (IASE) 2021 Article PeerReviewed Chan, Nicholas Kin Whai and Lee, Angela Siew Hoong * and Zuraini Zainol, (2021) A framework for predicting employee health risks using Ensemble Model. International Journal of Advanced and Applied Sciences, 8 (9). pp. 29-38. ISSN 2313-3724 http://doi.org/10.21833/ijaas.2021.09.004 doi:10.21833/ijaas.2021.09.004 |
| spellingShingle | QA75 Electronic computers. Computer science Chan, Nicholas Kin Whai Lee, Angela Siew Hoong * Zuraini Zainol, A framework for predicting employee health risks using Ensemble Model |
| title | A framework for predicting employee health risks using Ensemble Model |
| title_full | A framework for predicting employee health risks using Ensemble Model |
| title_fullStr | A framework for predicting employee health risks using Ensemble Model |
| title_full_unstemmed | A framework for predicting employee health risks using Ensemble Model |
| title_short | A framework for predicting employee health risks using Ensemble Model |
| title_sort | framework for predicting employee health risks using ensemble model |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.sunway.edu.my/1840/ http://eprints.sunway.edu.my/1840/ http://eprints.sunway.edu.my/1840/ |