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Predictive Analytics for OSA Detection Using Non-Conventional Metrics

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  • Vinit Kumar Gunjan

    (CMR Institute of Technology, India)

  • Madapuri Rudra Kumar

    (Annamacharya Institute of Technology and Sciences, Rajampet, India)

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.

Suggested Citation

  • Vinit Kumar Gunjan & Madapuri Rudra Kumar, 2020. "Predictive Analytics for OSA Detection Using Non-Conventional Metrics," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 10(4), pages 13-23, October.
    • Handle: RePEc:igg:jkbo00:v:10:y:2020:i:4:p:13-23
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