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• W3201601817 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." @default.
• W3201601817 created "2021-09-27" @default.
• W3201601817 creator A5070805897 @default.
• W3201601817 creator A5078279553 @default.
• W3201601817 creator A5090584812 @default.
• W3201601817 date "2021-09-15" @default.
• W3201601817 modified "2023-09-24" @default.
• W3201601817 title "Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers" @default.
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• W3201601817 doi "https://doi.org/10.3233/ida-205468" @default.
• W3201601817 hasPublicationYear "2021" @default.
• W3201601817 type Work @default.
• W3201601817 sameAs 3201601817 @default.

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