Predictive Analytics for OSA Detection Using Non-Conventional Metrics

Predictive Analytics for OSA Detection Using Non-Conventional Metrics

Vinit Kumar Gunjan, Madapuri Rudra Kumar
Copyright: © 2020 |Pages: 11
DOI: 10.4018/IJKBO.2020100102
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Abstract

Early diagnosis in the case of the sleep apnea has its own set of benefits for treating the cases. However, there are many challenges and limitations that impact the current conditions for testing. In this manuscript, a model is proposed for early diagnosis of OSA, using the non-conventional metrics. Profoundly, the metrics used are combination of symptoms, causes, and effects of the problem. Using a machine learning model and two sets of classifiers, the inputs collected as part of the training datasets are used for analysis. The data classifiers used for the model tests are NB and SVM. In a comparative analysis of the results, it is imperative that SVM classifier-based training of the proposed algorithm is giving more effective performance.
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Many research studies have focused on the distinct set of OSA evaluation models, the metrics that could be used in the process. There are a certain set of machine learning models too that were discussed in the obstructive sleep analysis conditions.

In a study conducted by Bozkurt, S., et. al., 2017 the results of the machine learning methods for the classification of OSA in the patients, using the breathing conditions as normal, mild or severe was evaluated using some of the non-PSG variables. The variables that are considered in the conditions are clinical data, physical examination records, and the symptoms.

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