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Practical strategies for large missing data in dementia diagnosis
[article]
2020
medRxiv
pre-print
Accurate computational models for clinical decision support systems require clean and reliable data, but in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. Many popular methods of handling missing data are unsuitable for handling such missing test data. This work addresses the problem by evaluating multiple imputation and classification workflows
doi:10.1101/2020.07.13.20146118
fatcat:um4mmasaubdxlg2u3toqs5sbaq