| Summary: | 碩士 === 中山醫學大學 === 醫學資訊學系碩士班 === 101 === In recent years, data mining technologies have matured and become more popular, providing a useful tool to analyze, classify and predict large amounts of data gathered from various fields. Early detection of medical problems is important to increase the chance of successful treatment. Such detection is often formulated as a binary classification problem. Various classification methods have been developed for the detection of a potential medical problem.
Sleep Apnea Syndrome (SAS) is a condition characterized by repeated episodes of apnea and hypopnea during sleep. The prevalence of OSA ranged from 25% to 27% in middle-aged men and from 10% to 16% in middle-aged women in Asian groups. Obstructive sleep apnea (OSA) is the most common category of SAS. OSA increases people’s risk of having heart diseases and sleep related accidents. Severe cases of OSA syndrome might cause patient deaths due to lack of sufficient oxygen intake.
OSA is diagnosed with an overnight sleep test called polysomnography (PSG). However, the availability of PSG evaluation is relatively limited to urban areas. Moreover, it is very time-consuming, expensive and tedious task consisting of expert visual evaluation all ten minutes pieces of approximately eight-hour recording with a setting of many channels.
There are some studies related to diagnosis of OSA in literature, but only 83, 86 and 110 patients and complex clinical features were used to building their models, and their performance had an accuracy of 74.2%~92.5%. The goal of this thesis is to find the best classifier of decision tree model using lower and noninvasive examination features on the diagnosis of OSA based on a larger clinical database. It will reduce overhead costs and examination duration for candidates or patients. According to the general physical check-up, the results can provide useful information to distinguish a patient who is the high risk patient or the low risk patient. Therefore, the decision result can help many low risk patients to avoid referring more complicated advanced examinations, it also can reduce medical overhead. Based on our proposed prediction model, the experiment results have shown that our model is more simple, accurate and reliable.
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