An analysis of ambulatory blood pressure monitoring using multi-label classification.

Ambulatory blood pressure monitoring (ABPM) involves measuring blood pressure by means of a tensiometer carried by the patient for a duration of 24 h, it currently occupies a central place in the diagnosis and follow-up of hypertensive patients, it provides crucial information which allows to make a specific diagnosis and adapt therapeutic attitude accordingly. The traditional analysis process suffers from different problems: it requires a lot of time and expertise, and several calculations should be performed manually by the expert, who is generally very busy. In this work, we attempt to improve the analysis of ABPM data using multi-label classification methods, where a record is associated with more than one label (class) at the same time. Seven algorithms are experimentally compared on a new multi-label ABPM-dataset. Experiments are conducted on 270 hypertensive patient records characterized by 40 attributes and associated with six labels. Results show that the multi-label modeling of ABPM data helps to investigate label dependencies and provide interesting insights, which can be integrated into the ABPM devices to dispense automatically detailed reports with possible future complications.