Predicting employee health risks using classification ensemble model - Chan, Nicholas Kin Whai; Lee, Angela Siew Hoong *; Zuraini Zainol, (2021)
Our planet is known as a digital earth, circulating around data. Growth in data is exponential, leading to an elevated interest in Big Data Analytics, to collect, store, process, analyze and visualize unparalleled amount of data. Modern information driven society will continue to be shaped by big data, where there will be potential to extract meaningful insights and hidden patterns impacting businesses in unforeseen measures. Most employers in Malaysia provide medical benefits which includes general medical costs to hospitalization benefits and insurance coverages; with these data and information stored by the HR (Human Resource), leading to a potential to analyze and identify patterns in historical claims - these insights would lead to improved decision making to better understand employee population health and the usage of the premium coverage. In predictive analysis, common techniques applied are Decision Tree and Regression. Therefore, the aim of this research is to propose a conceptual prediction model to better understand the patterns present in the employee healthcare data while predicting if an employee would be at any health risks to understand the population health and the usage of premium coverage provided by the employer. Additionally, to apply an ensemble method called Stacking, where multiple predictive models will be combined to perform a prediction. An ensemble model will present the opportunity to build a more robust and accurate model which could be applied across various industries instead of being industry specific.