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Intelligent Personalized Abnormality Detection for Remote Health Monitoring

Intelligent Personalized Abnormality Detection for Remote Health Monitoring

Poorani Marimuthu, Varalakshmi Perumal, Vaidehi Vijayakumar
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 23
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781799805120|DOI: 10.4018/IJIIT.2020040105
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MLA

Marimuthu, Poorani, et al. "Intelligent Personalized Abnormality Detection for Remote Health Monitoring." IJIIT vol.16, no.2 2020: pp.87-109. http://doi.org/10.4018/IJIIT.2020040105

APA

Marimuthu, P., Perumal, V., & Vijayakumar, V. (2020). Intelligent Personalized Abnormality Detection for Remote Health Monitoring. International Journal of Intelligent Information Technologies (IJIIT), 16(2), 87-109. http://doi.org/10.4018/IJIIT.2020040105

Chicago

Marimuthu, Poorani, Varalakshmi Perumal, and Vaidehi Vijayakumar. "Intelligent Personalized Abnormality Detection for Remote Health Monitoring," International Journal of Intelligent Information Technologies (IJIIT) 16, no.2: 87-109. http://doi.org/10.4018/IJIIT.2020040105

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Abstract

Machine learning algorithms are extensively used in healthcare analytics to learn normal and abnormal patterns automatically. The detection and prediction accuracy of any machine learning model depends on many factors like ground truth instances, attribute relationships, model design, the size of the dataset, the percentage of uncertainty, the training and testing environment, etc. Prediction models in healthcare should generate a minimal false positive and false negative rate. To accomplish high classification or prediction accuracy, the screening of health status needs to be personalized rather than following general clinical practice guidelines (CPG) which fits for an average population. Hence, a personalized screening model (IPAD – Intelligent Personalized Abnormality Detection) for remote healthcare is proposed that tailored to specific individual. The severity level of the abnormal status has been derived using personalized health values and the IPAD model obtains an area under the curve (AUC) of 0.907.

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