Final year projects (Baccalaureus Theses) (Industrial Engineering)
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Browsing Final year projects (Baccalaureus Theses) (Industrial Engineering) by Author "Van Zyl, Ilse"
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- ItemPaving the way for the use of prediction modelling in a health care environment(Stellenbosch : Stellenbosch University, 2011-10) Van Zyl, Ilse; Van Dyk, L.; Visser, T.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: The high cost of hospitalisation is a challenge for many health insurance companies, governments and individuals alike. In 2006, studies concluded that well over $30 billion was spent on unnecessary hospitalisations in the United States of America, where unnecessary hospitalisations are those that could have been prevented through early patient diagnosis and treatment. Undoubtedly, there is room for improvement in this regard and it can be agreed that where lives are at stake, prevention is always better than cure; successful hospitalisation prediction may make hospitalisation prevention a realistic possibility. The Heritage Provider Network, a health insurance and health care provider and sponsor of the Heritage Health Prize (HHP) Competition, have come to realise the potential benefits that a hospitalisation prediction model could effect (Heritage Provider Network Health Prize, 2011). The competition is aimed at producing an effective hospitalisation prediction patient admissions algorithm (PPAA) to predict the amount of days a member will be hospitalised in the next period using health insurance claims data of the current period. The goal is to ultimately prevent the unnecessary hospitalisation of identified members in their network. If successful this could have many benefits to the wider society including fewer critical medical cases, fewer claims and consequently lower expenses for all stakeholders in the affected system. The competition serves as inspiration for this study which aims to pave the way for the research team who will be developing such a PPAA. This was accomplished by providing insights and identifying possible pitfalls in the development of a Predictive Patient Admission Algorithm (PPAA) using the Heritage Health Prize case study as a reference. Typically available hospitalisation data that serves as input for the PPAA are briefly described, together with recommendations on methods and technologies with which to extract, transform and load (ETL) data within this context. A list of contender techniques was assembled based on the given data, the algorithm’s expected input requirements and the techniques’ ability to meet these needs. The prediction modelling techniques reviewed include classification and regression trees (CART), multivariate adaptive regression splines (MARS), neural networks and ensemble methods. Techniques were compared in terms of a set of criteria needed to use the available data and give the desired outputs. Page iv The data mining technologies considered to model with the preferred technique include Statistica data miner, SPSS Clementine, SAS Enterprise Miner, Matlab, Excel with VBA and R. These technologies were also compared on how well they can model available data with the contender techniques. The research team’s compatibility with technologies was also considered. Recommendations concerning the prediction modelling technique was using ensemble methods and the choice of technology for ETL was SQL Server and for prediction model building recommendations are Statistica, R or Matlab. Experimentation was conducted with selected CART, MARS and the Random Forests techniques in the available technologies in order to support future prediction modelling decisions of the research team. It was concluded that the included predictor variables do not have sufficient predictive power for the use of CART, MARS and Neural Networks and that Random Forests deliver more favourable results and it was recommended that this modelling should be explored further for the use of the HHP application.