Dataoriented approach to improve adherence to CPAP therapy during the initiation phase Obstructive Sleep Apnea (OSA) is a sleep pathology that leads to different illness. The goal therapy for OSA is a Continuous Positive Airway Pressure (CPAP). However, CPAP therapy is one of the therapies which has the lowest adherence level. This paper presents a datadriven framework to improve the experience of the patients during the initiation phase of CPAP therapy. Since this phase is a key factor for adherence level over time. Our approach uses data analytics techniques to provide personalised services for each patient through a different process of knowledge discovery. We have integrated a validation process for each outcome of the framework, Therefore, there is also continuous improvement of the data models.

Sleep Disorder Data Stream Classification Based on Classifiers Ensemble and Active Learning Polysomnography (PSG) screening for obstructive sleep apnea (OSA) is time consuming. The OSA classification is very important for medical scientists and machine learning researchers. In the current work, we developed a classification method for electrocardiogram (ECG) data. The data set has two labels: sleep disorders or not. As a result, Active Learning is used as a classification technique. Data stream classification in a nonstationary environment is attaining more attention recently. It is a highly challenging task, since the concept drift and limited labeled data. Therefore, a classification model is needed to be struggling with concept drift detection and the need of labeled data. To solve these issues, we propose an efficient semisupervised method in this paper which uses Active Learning to detect concept drift in an unsupervised way and Classifiers Ensemble to keep higher predictions combined with weighted majority voting. Experiments results on realworld and synthetic datasets show the effectiveness of the proposed approach. For the initial experiment, we use an existing data set. The data set includes data for every 10 seconds, up to 6000 seconds, and 35 patients. We have used 80% of the data for training purposes and 20% of the data for testing purposes. Active Learning results show that our method can effectively detect OSA. The accuracy of the predicted result is 71%. Future research in this area will be to obtain data from hospitals and use our developed algorithms for OSA classification and prediction.