Predictive Analytics for Obstructive Sleep Apnea Detection
M. V. Vijaya Saradhi1, Kalli Srinivasa Nageswara Prasad2
1Dr. M. V. Vijaya Saradhi, Professor, Deptmant Of CSE, ACE Engineering College, Ghatkesar, Hyderabad, India.
2Dr. Kalli Srinivasa Nageswara Prasad, Professor, Department CSE, Ramachandra College of Engineering, Eluru, Andhra Pradesh. India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 568-573 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11180789S219/19©BEIESP DOI: 10.35940/ijitee.I1118.0789S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Obstructive Sleep Apnea, ahi Index, Machine Learning, Contemporary Information System.
Scope of the Article: Community Information Systems