An Ensemble of Deep Auto-Encoders for Healthcare Monitoring

Ambient Intelligence (AmI) is a new paradigm that redefines interaction between humans, sensors and flow of data and information. In AmI, environment is more sensitive and more responsive to the user who acts spontaneously in the foreground, while sensors, machines and intelligent methods act in background. Behind the AmI interfaces, a huge volume of data is collected and analysed to make decision in real time. In the field of health care, AmI solutions prevent the patients from emergency situations by using data mining techniques. This paper assess the performance of deep learning against traditional dimensionality reduction and classification algorithms for healthcare monitoring in a hospital environment. An ensemble method is proposed using three classifiers on a reduced version of the dataset, where the dimensionality reduction technique is based on deep learning. The evaluation based on different performance metrics like accuracy, precision, recall and f-measure illustrated reliable performance of the proposed ensemble method.