The Application of Rough Set and Mahalanobis Distance to Enhance the Quality of OSA Diagnosis

doi:10.1016/j.eswa.2010.12.122

淡江大學機構典藏 > 商管學院 > 企業管理學系暨研究所 > 期刊論文 > Item 987654321/107032

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題名:	The Application of Rough Set and Mahalanobis Distance to Enhance the Quality of OSA Diagnosis
作者:	P.-C. Wang;C.-T. Su;K.-H. Chen;N.-H. Chen
關鍵詞:	Obstructive sleep apnea;Mahalanobis distance;Rough set
日期:	2011-12-01
上傳時間:	2016-08-15
出版者:	Pergamon Press
摘要:	This study aims to apply an analytical approach based on anthropometry and questionnaire data to detect obstructive sleep apnea (OSA). In recent years, OSA has become a pressing public health problem that demands serious attention. Approximately one in five American adults has at least mild OSA. In 2004, access economics estimated that in the Australian community, the cost of sleep disorders was over $7 billion, and much of this cost was related to OSA. Traditionally, a polysomnography (PSG) is considered to be a well-established and effective diagnosis for this disorder. However, PSG is time consuming and labor intensive as doctors require an overnight PSG evaluation in sleep laboratories with dedicated systems and attending personnel. Our proposed analytical approach is the integration of a rough set (RS) and the Mahalanobis distance (MD). RS was utilized to select important features, while MD was employed to distinguish the pattern of OSA. In this study, data were collected from 86 subjects (62 diseases and 24 non-diseases) who were referred for clinical suspicion of OSA. To grade the severity of the sleep apnea, the number of events per hour is reported as the apnea–hypopnea index (AHI). In the study, we define AHI < 5 as non-disease and AH ⩾ 5 as disease. According to sensitivity, specificity analysis, and g-means, the results show that our proposed method outperforms other methods such as logistic regression (LR), artificial neural networks (ANNs), support vector machine (SVM), and the C4.5 decision tree. Implementation results show that not only can our proposed method effectively detect OSA; it can reduce the cost and time needed for an accurate diagnosis. The proposed approach can be employed by physicians when providing the clinical decision for their patients.
關聯:	Expert Systems with Applications 38(6), pp.7828–7836
DOI:	10.1016/j.eswa.2010.12.122
顯示於類別:	[企業管理學系暨研究所] 期刊論文

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