The application of rough set and Mahalanobis distance to enhance the quality of OSA diagnosis

Authors:
Pa-Chun Wang;Chao-Ton Su;Kun-Huang Chen;Ning-Hung Chen
Affiliations:
Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan and Fu Jen Catholic University School of Medicine, Taipei, Taiwan and Department of Public Health, China Medical University, T ...;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan;Sleep Center, Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Chang Gung University, Taipei, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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 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.