Miswan, Nor Hamizah and Chan, Chee Seng and Ng, Chong Guan (2021) Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers. Intelligent Data Analysis, 25 (5). pp. 1073-1098. ISSN 1088-467X, DOI https://doi.org/10.3233/IDA-205468.
Full text not available from this repository.Abstract
Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model's prediction of hospital readmission.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya (IIRG004B-19HWB), Universiti Kebangsaan Malaysia, Ministry of Education, Malaysia |
| Uncontrolled Keywords: | Hospital readmission; Machine learning; Predictive modelling; Preprocessing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence Faculty of Medicine > Psychological Medicine Department |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 24 May 2022 08:27 |
| Last Modified: | 24 May 2022 08:27 |
| URI: | http://eprints.um.edu.my/id/eprint/27135 |
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