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dc.contributor.authorBillichová, Martina
dc.contributor.authorCoan, Lauren Joyce
dc.contributor.authorCzanner, Silvester
dc.contributor.authorKováčová, Monika
dc.contributor.authorSharifian, Fariba
dc.contributor.authorCzanner, Gabriela
dc.date.accessioned2024-09-09T12:21:01Z
dc.date.available2024-09-09T12:21:01Z
dc.date.issued2024-01-22
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/628991/journal.pone.0297190.pdf?sequence=2
dc.identifier.citationBillichová, M., Coan, L. J., Czanner, S., Kováčová, M., Sharifian, F., & Czanner, G. (2024). Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment. Plos one, 19(1), e0297190. http://dx.doi.org/10.1371/journal.pone.0297190en_US
dc.identifier.issn1932-6203en_US
dc.identifier.doi10.1371/journal.pone.0297190en_US
dc.identifier.urihttp://hdl.handle.net/10034/628991
dc.description.abstractMild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, that is beyond what is expected due to normal aging. Detection of MCI is crucial for providing appropriate interventions and slowing down the progression of dementia. There are several automated predictive algorithms for prediction using time-to-event data, but it is not clear which is best to predict the time to conversion to MCI. There is also confusion if algorithms with fewer training weights are less accurate. We compared three algorithms, from smaller to large numbers of training weights: a statistical predictive model (Cox proportional hazards model, CoxPH), a machine learning model (Random Survival Forest, RSF), and a deep learning model (DeepSurv). To compare the algorithms under different scenarios, we created a simulated dataset based on the Alzheimer NACC dataset. We found that the CoxPH model was among the best-performing models, in all simulated scenarios. In a larger sample size (n = 6,000), the deep learning algorithm (DeepSurv) exhibited comparable accuracy (73.1%) to the CoxPH model (73%). In the past, ignoring heterogeneity in the CoxPH model led to the conclusion that deep learning methods are superior. We found that when using the CoxPH model with heterogeneity, its accuracy is comparable to that of DeepSurv and RSF. Furthermore, when unobserved heterogeneity is present, such as missing features in the training, all three models showed a similar drop in accuracy. This simulation study suggests that in some applications an algorithm with a smaller number of training weights is not disadvantaged in terms of accuracy. Since algorithms with fewer weights are inherently easier to explain, this study can help artificial intelligence research develop a principled approach to comparing statistical, machine learning, and deep learning algorithms for time-to-event predictions.en_US
dc.description.sponsorshipAgentúra na podporu výskumu a vývojaen_US
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.language.isoen
dc.publisherPublic Library of Scienceen_US
dc.relation.urlhttp://dx.doi.org/10.1371/journal.pone.0297190en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectMild Cognitive Impairmenten_US
dc.subjectAlzheimer's Diseaseen_US
dc.subjectDementiaen_US
dc.subjectAgingen_US
dc.subjectMachine Learningen_US
dc.subjectArtificial Intelligence
dc.titleComparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairmenten_US
dc.typeArticleen_US
dc.identifier.eissn1932-6203en_US
dc.contributor.departmentSlovak University of Technology in Bratislava; Liverpool John Moores University; University of Chester;en_US
dc.identifier.journalPLoS ONEen_US
dc.date.updated2024-09-08T17:14:25Z
dc.identifier.volume19
dc.date.accepted2024-01-01
rioxxterms.identifier.projectAPVV-21-0448en_US
rioxxterms.versionVoRen_US
rioxxterms.typeJournal Article/Review
dc.source.issue1 January
dc.source.beginpagee0297190
dc.date.deposited2024-09-09en_US


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