Publicação
Optimizing referral screening in healthcare: an ensemble learning approach to improve classification accuracy at Hospital Garcia de Orta
| Resumo: | This research explores improving referral screening at Hospital Garcia de Orta (HGO) through ensemble learning. Initially, a single algorithmic model from Nova SBE's project-based learning initiative achieved a recall of 0.95 and a precision of 0.72. Enhancing this, the study applied ensemble learning techniques, significantly boosting performance. The resultant ensemble model reached a recall of 0.96 and precision of 0.87, demonstrating the effectiveness of ensemble learning in healthcare. This advancement emphasizes its importance in optimizing patient care and advancing healthcare information technology. |
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| Autores principais: | Haegh, Julie Dohlen |
| Assunto: | Business analytics Machine learning Decision-Making Healthcare Prediction Ensemble learning NIP Classification |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This research explores improving referral screening at Hospital Garcia de Orta (HGO) through ensemble learning. Initially, a single algorithmic model from Nova SBE's project-based learning initiative achieved a recall of 0.95 and a precision of 0.72. Enhancing this, the study applied ensemble learning techniques, significantly boosting performance. The resultant ensemble model reached a recall of 0.96 and precision of 0.87, demonstrating the effectiveness of ensemble learning in healthcare. This advancement emphasizes its importance in optimizing patient care and advancing healthcare information technology. |
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