The Use Genetic Algorithm to Predict Disease-A Case Study of Sleep Apnea Syndrome.

• Main Authors: Chuang Chih Yuan, 莊志遠
• Other Authors: Liu Li
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/75147837662337940303

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 94 === Sleep apnea syndrome is regarded as the most important sleep disorder discovered in the 20th century as well as the significant research direction for sleep medicine. Sleeping breath-ceasing results in a symptom of repeated arterial anoxemia in sleeping, which can easily cause harm to cardio- pulmonary and causes sequela and complicating disease such as excessive day-time sleepiness and drowsy driving, even sudden death in sleeping. In such situations, the disease not only consume a lot of medical resources but also has a bad impact on the patient’ living quality. Generally speaking, most of patients are unaware of sleep apnea syndrome for its painlessness and none- discomfort, which will delay treatment. Moreover, it requires for high costs and personnel expense on polysomnograph（PSG）examination. We could not do overall examination according to the contemporary medical resources. In the aspect of preventive medicine, it is necessary to establish a set of prediction model to accurately give preference to moderate serious sleep apnea syndrome. In this study, by means of computerized analyzing the laboratory data of the patients during medical visits we use Genetic Algorithms（GA）to solve complicated problems from changeable items of questionnaire to establish a system that can accurately predict moderate serious sleep apnea syndrome. The system can provide high-risk populations with convenient and precise predictive model so as to help medical personnel that they could propose the proper treatments and suggestions for patients according to various degrees of disease. The result of the research clearly shows that GA model is better than Logistic Regression that is widely used for establishing Disease Predictive model by scholars of epidemic diseases, and it brings more accurate effects than that of the later.