腦中風(stroke)為最常見威脅性命的神經疾病，也是造成全球死亡率增加的主要因素之一，全球五十億人口中約有兩千五百萬到三千萬人口曾經罹患腦中風，以死亡率而言，美國每年約有十六萬人因腦中風而死亡，而台灣每年約有一萬人因腦中風死亡。暫時性腦缺血的病患再度腦中風的機率為一般人的十倍，其死亡率更高達四分之一。此外，腦中風的復發也會明顯增加死亡率、無法工作人口比率與平均住院日。因此，本研究以典型相關分析瞭解腦中風危險因子與再發性腦中風嚴重度指標之相關性，並建構一套預測模式，作為醫師診療時參考的依據。由於預測腦中風再發生的過程是複雜多變的，變數間呈非線性關係，所以本研究採用類神經網路技術，以331筆病歷資料進行資料分析，再將資料進行3摺交叉驗證（3-fold cross validation），並比較羅吉斯迴歸與類神經網路之預測績效。分析結果發現，當病患有效控制血壓，則會降低腦中風復發時的嚴重程度，而且有心臟病史的病患會影響其預後的狀況。最後，本研究以準確度、敏感度與特異度評估預測模式的績效，分析結果分別如下類神經網路模型之準確度94.8％（敏感度93.9％，特異度95.7％）；羅吉斯迴歸模型為94.6％（敏感度92.3％，特異度96.3％）。

Stroke is one of the life-threatening neuropathy. It is the key factor to increase mortality rate over the world. Two thousands five hundred million people suffered from stroke on earth. Among these figures, one hundred sixty thousand people died of stroke in America and ten thousand people died each year in Taiwan. Patients who had Transient ischaemic attack(TIA) suffered from recurrent stroke is ten times more. Their mortality rate is higher than one-fourth. Additionally, stroke recurrence is a significant concern with regard to an increase in mortality, disability, and length of hospital stay. Thus, correlation analysis can be used to look at the relationships between stroke risk factors and the severity score of recurrent stroke. This built system assist the physicians in diagnosis.
To predict the stroke recurrence is a complex task and it is a nonlinear relationship among many variables. We developed an ANN model to assist the physicians to predict the possibility of stroke recurrence. The study is retrospective by using information from a database of medical inpatients. Three hundred and thirty one patients’ records were used as sample. To achieve optimum performance, we use a three-fold cross validation procedure. Furthermore, we compared the performance of ANN against the logistic regression approach on the same dataset. Our results show that patients with well control blood pressure will have lower severity score. Finally, we evaluated the performance of models according to prediction accuracy, sensitivity and specificity.

中文部份:
葉怡成，類神經網路模式應用與實作（八版），儒林圖書有限公司，2003年。
羅華強，類神經網路-MATLAB的應用，高立圖書有限公司，2005年。
行政院衛生署，國際疾病統計分類，2005年。
榮泰生，SPSS與研究方法（十版），五南圖書出版公司，2006年。
陳順宇，多變量分析（三版），華泰書局，2004年。
英文部份:
Benson, R. T. and Sacco, R. L., “Stroke Prevention：Hypertension, Diabetes, Tobacco, and Lipids,” Neurologia Clinics, 18, 309-319, 2000.
Chiang, D., Chiang, H.-C., Chen, W.-C. and Tsai, F.-J., “Prediction of Stone Disease by Discriminant Analysis and Artificial Neural Networks in Genetic Polymorphisms：a New Method,” BJU International, 91, 661-666, 2003.
Cooper, G. F., Abraham, V., Aliferis, C. F., Aronis, J. M., Buchanan, B. G., Caruana, R., Fine, M. J., Janosky, J. E., Liningston, G., Mitchell, T., Monti, S. and Spirtes, P., “Predicting Dire Outcomes of Patients with Community Acquired Pneumonia,” Journal of Biomedical Informatics, 38, 347-366, 2005.
Cubrilo-Turek, M., “Stroke Risk Factors：Recent Evidence and New Aspects,” International Congress Series, 1262, 466-469, 2004.
Delen, D., Walker, G. and Kadam, A., “Predicting Breast Cancer Survivability：A Comparison of Three Data Mining Methods ,” Artificial Intelligence in Medicine, 34, 113-127, 2005.
Do, S. R., Richardson, D., Hung, E., Brorson, J. R., Cruz-Flores, S., Felton III, W. L., Ford-Lynch, G., Helgason, C., Hsu, C., Kramer, J., Mitsias, P. and Gorelick, P.B., “Predictors of Recurrent Stroke in African Americans,” Neurology, 67, 567-571, 2006.
Du, X., McNamee, R. and Cruickshank, K., “Stroke Risk from Multiple Risk Factors Combined with Hypertension：A Primary Care Based Case-Control Study in a Defined Population of Northwest England ,” Annals of Epidemiology , 10, 380-388, 2000.
Ergun, U., Serhatlioglu, S., Hardalac, F. and Guler, I., “Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression ,” Computer in Biology and Medicine , 34 , 389-405, 2004.

Fatahzadeh, M. and Glick, M., “Oral Medicine,” Oral surgery Oral Medicine Oral Pathology Oral Radiology and Endodontics, 102, 180-191, 2006.
Feigin, V. L., Lawes, C. M., Bennett, D. A. and Anderson, C. A., “Stroke Epidemiology：A Review of Poptlarion-Based Studies of Incidence, Prevalence, and Case-Fatality in the Late 20th Century,” Lancet Neurology, 2, 43-53, 2003.
Garcia-Perez, E., Violante, A. and Cervantes-Perez, F., “Using Neural Networks for Differential Diagnosis of Alzheimer Disease and Vascular Dementia,” Expert Systems with Applications, 14, 219-225, 1998.
Gorelick, P. S., Sacco, R. L., Smith, D. B., Alberts, M., Mustone-Alexander, L., Rader, D., Ross, J. L., Raps, E., Oaer, M. N., Brass, L. M., Malone, M. E., Goldberg, S. and Booss, J., “Prevention of a First Stroke：A Review of Guidelines and a Multidisciplinary Consensus Statement From the National Stroke Association,” Journal of the American Medical Association, 281, 1112-1120, 1999.
Gottrup, C., Thomsen, K., Locht, P., Wu, O., Sorensen, A. G., Koroshetz, W. J. and Ostergaard, L., “Applying Instance-based Techniques to Prediction of Final Outcome in Acute Stroke,” Artificial Intelligence in Medicine, 33, 223-236, 2005.
Gulliford, S., Webb, S., Rowbottom , C. G., Corne, D. W. and Dearnaley, D. P., “Use of Artificial Neural Networks to Predict Biological Outcomes for Patient Receiving Radical Radiotherapy of the Prostate ,” Radiotherapy and Oncology, 71, 3-12, 2004.
Hagan, M. T. and Menhaj, M. B., “Training Feedforward Networks with the Marquardt Algorithm ,” IEEE Transactions on Neural Networks, 5, 989-993, 1994.
Hata, J., Tanizaki, Y. and Kiyohara, Y., “Ten Year Recurrence after First-ever Stroke in a Japanese Community：The Hisayama Study, ” Journal of Neurology Neurosurgery and Psychiatry, 76, 368-372, 2005.
Huang, Z. S., Chiang, T. L. and Lee, T. K., “Stroke Prevalence in Taiwan findings from the 1994 National Health Interview Survey,” Stroke, 28, 1579-1584, 1997.
Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., Schwab, M., Antonescu, C. R., Peterson, C. and Meltzer, P. S., “Classification and Diagnostic Prediction of Cancers using Gene Expression Profiling and Artificial Neural Networks,” Nature Medicine, 7, 673-679, 2001.
Kirshner, H. S., “Medical Prevention of Stroke,” Southern Medical Journal, 96, 354-358, 2003.
Kumar, U. A., “Comparison of Neural Networks and Regression Analysis：A New Insight ,” Expert Systems with Applications, 29, 424-430, 2005.
Lee, T. L., “Back-propagation Neural Network for Long-Term Tidal Predictions,” Ocean Engineering, 31,225-238, 2004.
Nedeltchev, K., Der Maur, T. A.and Georgiadis, D., “ Ischaemic Stroke in Young Adults： Predictors of Outcome and Recurrence, ” Journal of Neurology Neurosurgery and Psychiatry, 76, 191-195, 2005.
Rodgers, A., MacMahon, S., Yee, T. and Clark, T., “Blood Pressure, Cholesterol and Stroke in Eastern Asia,” Lancet, 352, 1801-1807, 1998.
Rothwell, P. M., Giles, M. F., Flossmann, E., Lovelock, C. E., Redgrave, J. N. E., Warlow, C. P. and Mehta, Z., “A Simple Score(ABCD) to identify Individuals at High Early Risk of Stroke after Transient Ischemic Attack,” Lancet, 366, 29-36, 2005.
Santos-Garcia, G., Varela G., Novoa N. and Jimenez M. F., “Prediction of Postoperative Morbidity after Lung Resection using an Artificial Neural Network Ensemble,” Artificial Intelligence in Medicine, 30, 61-69, 2004.

Schneider, A. T., Pancioli, A. M., Khoury, J. C., Rademacher, E., Tuchfarber, A., Miller, R., Woo, D., Kissela, B. and Broderick, J. P., “Trends in Community Knowledge of the Warning Signs and Risk Factors for Stroke,” Journal of the American Medical Association , 289, 343-346, 2003.
Soyama, Y., Miura, K., Morikawa, Y., Nishijo, M., Nakanishi, Y., Naruse, Y., Kagamimori, S. and Nakagawa, H., “High-density Lipoprotein Cholesterol and Risk of Stroke in Japanese Men and Women：the Oyaba Study,” Stroke, 34, 863-868, 2003.
Tomida, S., Hanai, T., Koma, N., Suzuki Y., Kobayashi, T. and Honda H., “Artificial Neural Network Predictive Model for Allergic Disease Using Single Nucleotide Polymorphisms Data,” Journal of Bioscience Bioengineering, 93, 470-478, 2002.
Ture, M., Kurt, I., Kurum, A. T. and Ozdamar, K., “Comparing Classification Techniques for Predicting Essential Hypertension,” Expert Systems with Applications, 29, 583-588, 2005.
Wang, Y., Lim, L. L.-Y., Heller, R. F., Fisher, J. and Levi, C. R., “A Prediction Model of 1-year Mortality for Acute Ischemic,” Archives of Physical Medicine and Rehabilitation, 84, 1006-1011, 2003.
West, P. M., Brockett, P. L. and Golden L. L., “A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice ,” Marketing Science, 16, 370-391, 1997.
Zhang, X.-F., Attia, J., D’Este, C., Yu, X.-H. and Wu, X.-G., “A Risk Score Predicted Coronary Heart Disease and Stroke in a Chinese Cohort,” Journal of Clinical Epidemiology, 58, 951-958, 2005.
相關網址:
行政院衛生署統計室網址：http://www.doh.gov.tw/statistic/data/死因摘要/94年/表1.xls, 2006/09/02.
行政院衛生署統計室網址：http://www.doh.gov.tw/statistic/data/衛生統計重要指標/94/8.xls, 2006/08/22.
美國心臟醫學會：http://www.americanheart.org/downloadable/heart/ppt, 2006/09/11.
台灣地區2010年衛生指標白皮書：http://www.doh.gov.tw/ufile/doc/200601_台灣地區2010年衛生指標白皮書(94年版).pdf, 2006/10/25.