Predictive analytics for obstructive sleep apnea detection

Abstract

Many of the research studies that have focused on the issue of sleep apnea conditions among the people, emphasize the fact that the numbers are rising in significant numbers year on year. Profoundly, identifying symptoms in the patients is very important to ascertain the possible impact of sleep apnea in patients. The researchers in earlier studies have focused on the conditions of systematical physical examination over the patients who are prone to physical examination for head and neck aches, has relative impact of the osa conditions and also on some scoring-based models using the machine learning solutions. The scope of a new model could be about identification of the features in two stage model. The first stage could be about understanding the lifestyle and psychological conditions of the patient data and accordingly choose the metrics and the model of osa detection tool that can be used for analysis. If such comprehensive approach can be developed, it can be effective process for developing a sustainable solution.