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研究生:王致程
研究生(外文):Wang,Chih-Cheng
論文名稱:以CART與多重SVM探討腦出血影響因子與三十天腦出血死亡率
論文名稱(外文):To explore Intracerebral hemorrhage and 30-day mortality with CART and multi-SVM
指導教授:邱登裕邱登裕引用關係
指導教授(外文):Chiu,Deng-Yiv
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:41
中文關鍵詞:腦出血ICH Score分類與回歸樹模糊分群支持向量機
外文關鍵詞:Intracerebral HemorrhageICH ScoreClassification And Regression TreeFuzzy C-meansSupport Vector Machine
相關次數:
  • 被引用被引用:3
  • 點閱點閱:366
  • 評分評分:
  • 下載下載:52
  • 收藏至我的研究室書目清單書目收藏:0
腦出血(ICH)在腦中風患者中佔了10至15%,屬於一種致命的中風病症。然而,目前臨床上使用一種將腦出血因子量尺化的規則稱作ICH score,作為三十天腦出血死亡率的判斷依據,然而ICH score中的因子是否具有判別性一直是許多學者關心的議題。
本研究目的在於找出影響腦出血的影響因子組合,將相近特徵的腦出血患者進行分群,並探討腦出血影響因子和三十天腦出血死亡率之間的關係。
實驗資料為137個腦出血病歷資料,本研究先使用決策樹找到辨別腦出血因素的因子組合。再將腦出血資料使用模糊分群(FCM)進行分群,接著為每一群資料構建支持向量機(SVM)。最後,我們指定每個測試資料到其相應的群聚,再使用該群聚所對應的SVM分類器來探討影響因子組合與三十天腦出血死亡率之間的關係。

Intracerebral hematoma(ICH) occupies 10~15% of stroke patients. It is a kind of fatal stroke disorder. There is a measurement factor, called ICH score, used to explore the 30-day mortality. Wether the ICH score is a discriminative factor is an issue to study.
The objective of this study is to find discriminative combination of impact factors of intracerebral hematoma (ICH) to cluster ICH patients with similar features to explore relationship among impact factors and 30-day mortality of ICH.
The data of 137 ICH patients are collected. We use a decision tree to find discriminative combination of the impact factors. We cluster ICH patients with similar features using Fuzzy C-means algorithm (FCM) to construct a support vector machine (SVM) for each cluster. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality.

摘要 I
ABSTRACT II
目錄 III
圖目錄 IV
表目錄 V
第一章 諸論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究架構 3
第二章 文獻探討 5
第一節 ICH(Intracerebral Hemorrhage) Score 5
第二節 CART (Classification And Regression Tree) 7
第三節 FCM (Fuzzy c-means) 12
第四節 SVM (Support Vector Machine) 16
第三章 研究方法 22
第一節 CART(Classification And Regression Tree) 24
第二節 FCM(Fuzzy C-means) 25
第三節 建置多重支持向量機與資料預測 26
第四章 實驗結果 28
第一節 實驗資料 28
第二節 CART分析結果 28
第三節 FCM分群結果 31
第四節 模型建構與評估 33
第五章 結論與未來展望 35
第一節 結論 35
第二節 未來展望 37
參考文獻 38

1. Anderson, C. S., Heeley, E., Huang, Y., Wang, J., Stapf, C., Delcourt, C., Investigators, I. (2013). Rapid blood-pressure lowering in patients with acute intracerebral hemorrhage. N Engl J Med, 368(25), 2355-2365. doi: 10.1056/NEJMoa1214609
2. Barlin, J. N., Zhou, Q., St Clair, C. M., Iasonos, A., Soslow, R. A., Alektiar, K. M., Abu-Rustum, N. R. (2013). Classification and regression tree (CART) analysis of endometrial carcinoma: Seeing the forest for the trees. Gynecol Oncol, 130(3), 452-456. doi: 10.1016/j.ygyno.2013.06.009
3. Bezdek, J. C. (1974). Cluster validity with fuzzy sets. Journal of Cybernetics, 3, 58-73.
4. Breiman, L. (1984). Classification and regression trees. Belmont, Calif.: Wadsworth International Group.
5. Chuang, Y. C., Chen, Y. M., Peng, S. K., & Peng, S. Y. (2009). Risk stratification for predicting 30-day mortality of intracerebral hemorrhage. Int J Qual Health Care, 21(6), 441-447. doi: 10.1093/intqhc/mzp041
6. Dumont, T. M., Rughani, A. I., & Tranmer, B. I. (2011). Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurg, 75(1), 57-63; discussion 25-58. doi: 10.1016/j.wneu.2010.07.007
7. Fletcher, R. (1987). Practical methods of optimization (2nd ed.). Chichester: Wiley.
8. Hemphill, J. C., 3rd, Bonovich, D. C., Besmertis, L., Manley, G. T., & Johnston, S. C. (2001). The ICH Score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke, 32(4), 891-897.
9. Ibrikci, T., Ustun, D., & Kaya, I. E. (2012). Diagnosis of several diseases by using combined kernels with Support Vector Machine. J Med Syst, 36(3), 1831-1840. doi: 10.1007/s10916-010-9642-5
10. Jung, S. Y., Vitolins, M. Z., Fenton, J., Frazier-Wood, A. C., Hursting, S. D., & Chang, S. (2015). Risk profiles for weight gain among postmenopausal women: a classification and regression tree analysis approach. PLoS One, 10(3), e0121430. doi: 10.1371/journal.pone.0121430
11. Kobayashi, D., Yokota, K., Takahashi, O., Arioka, H., & Fukui, T. (2014). A predictive rule for mortality of inpatients with Staphylococcus aureus bacteraemia: A classification and regression tree analysis. Eur J Intern Med, 25(10), 914-918. doi: 10.1016/j.ejim.2014.10.003
12. Liu, H., Zhang, C. M., Su, Z. Y., Wang, K., & Deng, K. (2015). Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification. Comput Math Methods Med, 2015, 185726. doi: 10.1155/2015/185726
13. Lukic, S., Cojbasic, Z., & Milosevic, Z. (2014). Comparation of artificial neural network and logistic regression models for predicting clinically relevant outcome. World Neurosurg, 82(1-2), e377-378. doi: 10.1016/j.wneu.2012.07.005
14. Mohammadpour, R., Shaharuddin, S., Chang, C. K., Zakaria, N. A., Ab Ghani, A., & Chan, N. W. (2015). Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res Int, 22(8), 6208-6219. doi: 10.1007/s11356-014-3806-7
15. Morgenstern, L. B., Hemphill, J. C., 3rd, Anderson, C., Becker, K., Broderick, J. P., Connolly, E. S., Jr., . . . Council on Cardiovascular, N. (2010). Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 41(9), 2108-2129. doi: 10.1161/STR.0b013e3181ec611b
16. Naidech, A. M. (2011). Intracranial hemorrhage. Am J Respir Crit Care Med, 184(9), 998-1006. doi: 10.1164/rccm.201103-0475CI
17. Naidech, A. M., Liebling, S. M., Duran, I. M., Moore, M. J., Wunderink, R. G., & Zembower, T. R. (2012). Reliability of the validated clinical diagnosis of pneumonia on validated outcomes after intracranial hemorrhage. J Crit Care, 27(5), 527 e527-511. doi: 10.1016/j.jcrc.2011.11.009
18. Parry-Jones, A. R., Abid, K. A., Di Napoli, M., Smith, C. J., Vail, A., Patel, H. C., . . . Tyrrell, P. J. (2013). Accuracy and clinical usefulness of intracerebral hemorrhage grading scores: a direct comparison in a UK population. Stroke, 44(7), 1840-1845. doi: 10.1161/STROKEAHA.113.001009
19. Peng, S. Y., Chuang, Y. C., Kang, T. W., & Tseng, K. H. (2010). Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination. Eur J Neurol, 17(7), 945-950. doi: 10.1111/j.1468-1331.2010.02955.x
20. Rashid, H. U., Amin, R., Rahman, A., Islam, M. R., Hossain, M., Barua, K. K., & Hossain, M. A. (2013). Correlation between intracerebral hemorrhage score and surgical outcome of spontaneous intracerebral hemorrhage. Bangladesh Med Res Counc Bull, 39(1), 1-5.
21. Sabit, H., & Al-Anbuky, A. (2014). Multivariate spatial condition mapping using subtractive fuzzy cluster means. Sensors (Basel), 14(10), 18960-18981. doi: 10.3390/s141018960
22. Salihovic, D., Smajlovic, D., & Ibrahimagic, O. C. (2013). Does the volume and localization of intracerebral hematoma affect short-term prognosis of patients with intracerebral hemorrhage? ISRN Neurosci, 2013, 327968. doi: 10.1155/2013/327968
23. Schneider, D. F., Dobrowolsky, A., Shakir, I. A., Sinacore, J. M., Mosier, M. J., & Gamelli, R. L. (2012). Predicting acute kidney injury among burn patients in the 21st century: a classification and regression tree analysis. J Burn Care Res, 33(2), 242-251. doi: 10.1097/BCR.0b013e318239cc24
24. Singh, A., & Guttag, J. V. (2011). A comparison of non-symmetric entropy-based classification trees and support vector machine for cardiovascular risk stratification. Conf Proc IEEE Eng Med Biol Soc, 2011, 79-82. doi: 10.1109/IEMBS.2011.6089901
25. Speiser, J. L., Lee, W. M., Karvellas, C. J., & Group, U. S. A. L. F. S. (2015). Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models. PLoS One, 10(4), e0122929. doi: 10.1371/journal.pone.0122929
26. Spyridonos, P., Gaitanis, G., Tzaphlidou, M., & Bassukas, I. D. (2014). Spatial fuzzy c-means algorithm with adaptive fuzzy exponent selection for robust vermilion border detection in healthy and diseased lower lips. Comput Methods Programs Biomed, 114(3), 291-301. doi: 10.1016/j.cmpb.2014.02.017
27. Steiner, T., Al-Shahi Salman, R., Beer, R., Christensen, H., Cordonnier, C., Csiba, L., . . . Wagner, M. (2014). European Stroke Organisation (ESO) guidelines for the management of spontaneous intracerebral hemorrhage. Int J Stroke, 9(7), 840-855. doi: 10.1111/ijs.12309
28. Vapnik VN, G. S., Smol A. (1997). Support vector method for function approximation, regression estimation and signal processing. 281-287.
29. Wang, C. W., Liu, Y. J., Lee, Y. H., Hueng, D. Y., Fan, H. C., Yang, F. C., . . . Hsu, H. H. (2014). Hematoma shape, hematoma size, Glasgow coma scale score and ICH Score: which predicts the 30-day mortality better for intracerebral hematoma? PLoS One, 9(7), e102326. doi: 10.1371/journal.pone.0102326
30. Wang, W., Lu, J., Wang, C., Wang, Y., Li, H., Zhao, X., & Investigators for the China National Stroke Registry, I. (2013). Prognostic value of ICH Score and ICH-GS score in Chinese intracerebral hemorrhage patients: analysis from the China National Stroke Registry (CNSR). PLoS One, 8(10), e77421. doi: 10.1371/journal.pone.0077421
31. Wu, J., Qian, Z., Tao, L., Yin, J., Ding, S., Zhang, Y., & Yu, Z. (2015). Resting state fMRI feature-based cerebral glioma grading by support vector machine. Int J Comput Assist Radiol Surg, 10(7), 1167-1174. doi: 10.1007/s11548-014-1111-z
32. Yu, S., Tan, K. K., Sng, B. L., Li, S., & Sia, A. T. (2015). Lumbar Ultrasound Image Feature Extraction and Classification with Support Vector Machine. Ultrasound Med Biol. doi: 10.1016/j.ultrasmedbio.2015.05.015
33. Zhang, J., & Shen, L. (2014). An improved fuzzy c-means clustering algorithm based on shadowed sets and PSO. Comput Intell Neurosci, 2014, 368628. doi: 10.1155/2014/368628
34. Zhong, M., Chong, Y., Nie, X., Yan, A., & Yuan, Q. (2013). Prediction of sweetness by multilinear regression analysis and support vector machine. J Food Sci, 78(9), S1445-1450. doi: 10.1111/1750-3841.12199
35. Zhou, J., Wu, X. M., & Zeng, W. J. (2015). Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine. J Clin Monit Comput. doi: 10.1007/s10877-015-9664-0

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