DocumentCode :
1823733
Title :
Development of an Intelligent App for Obstructive Sleep Apnea Prediction on Android Smartphone Using Data Mining Approach
Author :
Tseng, Ming-Hseng ; Hsu, Hsueh-Chen ; Chang, Che-Chia ; Ting, Hua ; Wu, Hui-Ching ; Tang, Ping-Hung
Author_Institution :
Sch. of Med. Inf., Chung-Shan Med. Univ., Taichung, Taiwan
fYear :
2012
fDate :
4-7 Sept. 2012
Firstpage :
774
Lastpage :
779
Abstract :
In recent years, sleep apnea syndrome is considered an important research direction in sleep medicine. According to statistics, the disease prevalence of obstructive sleep apnea (OSA) is more than 3% of the total population and even up to 25% for 40-aged men. More and more clinical evidences showed that obstructive sleep apnea is highly associated with hypertension, diabetes, metabolic syndrome, cardiovascular disease, nocturnal enuresis, and even depression. If we can detect the potential OSA patients early, and offering them appropriate treatments, it is worth not only promoting the quality of patient\´s life, but also reducing the possible serious complications and medical costs. Mobile phones are now playing an ever more crucial role in people\´s daily lives. The latest generation of smart phones is increasingly viewed as handheld computers rather than as phones, due to their powerful on-board computing capability, capacious memories, large screens and open operating systems that encourage applications (Apps) development. In this study, an intelligent "OSA prediction App" on Android Smart phone has been developed based on medical decision rules from a clinical large dataset. The proposed application can provide an easy and efficient way to quickly pre-screen high-risk groups of OSA potential patients, aid medical works to achieve early diagnosis and treatment purposes, prevent the occurrence of complications, and thus reach the goal of preventive medicine.
Keywords :
cardiovascular system; data mining; decision theory; diseases; medical computing; mobile computing; mobile handsets; operating systems (computers); patient treatment; sleep; OSA; android smartphone; cardiovascular disease; data mining; depression; diabetes; hypertension; intelligent application development; medical decision rule; metabolic syndrome; mobile phone; nocturnal enuresis; obstructive sleep apnea prediction; open operating system; patient treatment; sleep apnea syndrome; sleep medicine; Data mining; Decision trees; Educational institutions; Medical diagnostic imaging; Sleep apnea; Smart phones; Android Smartphone; Apps; data mining; decision rules; obstructive sleep apnea;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2012 9th International Conference on
Conference_Location :
Fukuoka
Print_ISBN :
978-1-4673-3084-8
Type :
conf
DOI :
10.1109/UIC-ATC.2012.89
Filename :
6332082
Link To Document :
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