AI-enhanced lung cancer prediction: a hybrid model's precision triumph
| dc.contributor.author | Kesiku, Cyrille | |
| dc.contributor.author | García-Zapirain, Begoña | |
| dc.date.accessioned | 2026-10-02T09:19:42Z | |
| dc.date.available | 2026-10-02T09:19:42Z | |
| dc.date.issued | 2025-09 | |
| dc.date.updated | 2026-10-02T09:19:42Z | |
| dc.description.abstract | Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5%, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine. | en |
| dc.identifier.citation | Kesiku, C. Y., & Garcia-Zapirain, B. (2025). AI-enhanced lung cancer prediction: a hybrid model’s precision triumph. IEEE Journal of Biomedical and Health Informatics, 29(9), 6287-6300. https://doi.org/10.1109/JBHI.2024.3447583 | |
| dc.identifier.doi | 10.1109/JBHI.2024.3447583 | |
| dc.identifier.eissn | 2168-2208 | |
| dc.identifier.issn | 2168-2194 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14454/6720 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.rights | © 2024 The Authors | |
| dc.subject.other | CNN BiLSTM attention | |
| dc.subject.other | Deep learning model | |
| dc.subject.other | Lung cancer | |
| dc.subject.other | Natural language processing | |
| dc.subject.other | Text classification | |
| dc.title | AI-enhanced lung cancer prediction: a hybrid model's precision triumph | en |
| dc.type | journal article | |
| dcterms.accessRights | open access | |
| oaire.citation.endPage | 6300 | |
| oaire.citation.issue | 9 | |
| oaire.citation.startPage | 6287 | |
| oaire.citation.title | IEEE Journal of Biomedical and Health Informatics | |
| oaire.citation.volume | 29 | |
| oaire.licenseCondition | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| oaire.version | VoR |
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