Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients
This work addresses the need for automated demographic analysis in healthcare, specifically for diabetic patients, but it appears incremental as it extends existing binary classification methods to multi-label settings without major breakthroughs.
The paper tackled the problem of classifying demographic attributes of diabetic patients as a multi-label classification task, applying ensembles of multi-label learning algorithms to identify groups likely to be diagnosed with diabetes, though no concrete numerical results were provided.
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.