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- W2269152073 abstract "Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where it can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multi-label learning algorithms." @default.
- W2269152073 created "2016-06-24" @default.
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- W2269152073 date "2015-03-26" @default.
- W2269152073 modified "2023-09-27" @default.
- W2269152073 title "Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients." @default.
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