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Hospital readmission risk
Hospital readmissions are a significant challenge in healthcare, as they lead to in creased costs, higher risk of mortality, treatment complications, and patient dis tress. This minor dissertation, set within the South African healthcare framework, investigates the potential of both traditional clin...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2025
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| _version_ | 1867614440545845248 |
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| access_status_str | Open Access |
| author | Mugova, Amos |
| author2 | Salau, Sulaiman |
| author_browse | Mugova, Amos Salau, Sulaiman |
| author_facet | Salau, Sulaiman Mugova, Amos |
| author_sort | Mugova, Amos |
| collection | Thesis |
| description | Hospital readmissions are a significant challenge in healthcare, as they lead to in creased costs, higher risk of mortality, treatment complications, and patient dis tress. This minor dissertation, set within the South African healthcare framework, investigates the potential of both traditional clinical screening tools and advanced statistical learning methods for predicting hospital readmission risk. The meth ods considered include the LACE score, decision trees, logistic regression, random forests, gradient-boosting methods, and neural networks. The study uses data from South Africa's privately insured demographic, provided by a private insurer. It includes a comprehensive array of patient information such as demographics, prescribed medications, medical procedures undergone, and historical hospital usage. Feature selection methods were used to identify relevant variables for model training, and the effectiveness of these variables was assessed based on their ability to differentiate between patients at risk of hospital readmission within 30 days after discharge. The statistical learning methods' efficacy was measured using several performance indicators, such as prediction accuracy, F1 score, Area Under the Receiver Operating Characteristics Curve (AUC), Area Under the Precision-Recall Curve (AUC-PR), and the Matthews Correlation Coefficient (MCC). The study found that the neural network model outperformed the other statistical learning methods evaluated across various metrics. Moreover, the research extends the range of variables used to predict hospital read missions beyond the traditional LACE score, incorporating critical factors such as the frequency and costs of previous hospital visits, expenses related to specialist services, patient age, and the primary diagnosis category. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/41108 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:52:04.898Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/41108 Hospital readmission risk Mugova, Amos Salau, Sulaiman Er, Sebnem data science Hospital readmissions are a significant challenge in healthcare, as they lead to in creased costs, higher risk of mortality, treatment complications, and patient dis tress. This minor dissertation, set within the South African healthcare framework, investigates the potential of both traditional clinical screening tools and advanced statistical learning methods for predicting hospital readmission risk. The meth ods considered include the LACE score, decision trees, logistic regression, random forests, gradient-boosting methods, and neural networks. The study uses data from South Africa's privately insured demographic, provided by a private insurer. It includes a comprehensive array of patient information such as demographics, prescribed medications, medical procedures undergone, and historical hospital usage. Feature selection methods were used to identify relevant variables for model training, and the effectiveness of these variables was assessed based on their ability to differentiate between patients at risk of hospital readmission within 30 days after discharge. The statistical learning methods' efficacy was measured using several performance indicators, such as prediction accuracy, F1 score, Area Under the Receiver Operating Characteristics Curve (AUC), Area Under the Precision-Recall Curve (AUC-PR), and the Matthews Correlation Coefficient (MCC). The study found that the neural network model outperformed the other statistical learning methods evaluated across various metrics. Moreover, the research extends the range of variables used to predict hospital read missions beyond the traditional LACE score, incorporating critical factors such as the frequency and costs of previous hospital visits, expenses related to specialist services, patient age, and the primary diagnosis category. 2025-03-05T11:11:08Z 2025-03-05T11:11:08Z 2024 2025-03-05T09:26:06Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/41108 eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | data science Mugova, Amos Hospital readmission risk |
| thesis_degree_str | Master's |
| title | Hospital readmission risk |
| title_full | Hospital readmission risk |
| title_fullStr | Hospital readmission risk |
| title_full_unstemmed | Hospital readmission risk |
| title_short | Hospital readmission risk |
| title_sort | hospital readmission risk |
| topic | data science |
| url | http://hdl.handle.net/11427/41108 |
| work_keys_str_mv | AT mugovaamos hospitalreadmissionrisk |