請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20468
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 周呈霙(Cheng-Ying Chou) | |
dc.contributor.author | Yu-Han Cheng | en |
dc.contributor.author | 鄭羽涵 | zh_TW |
dc.date.accessioned | 2021-06-08T02:49:45Z | - |
dc.date.copyright | 2017-08-24 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-16 | |
dc.identifier.citation | [1] N. Chalasani et al., 'The diagnosis and management of non-alcoholic fatty liver disease: practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association,' Hepatology, vol. 55, no. 6, pp. 2005-23, Jun 2012.
[2] D. L. White, F. Kanwal, and H. B. El-Serag, 'Association between nonalcoholic fatty liver disease and risk for hepatocellular cancer, based on systematic review,' Clin Gastroenterol Hepatol, vol. 10, no. 12, pp. 1342-1359 e2, Dec 2012. [3] G. A. Michelotti, M. V. Machado, and A. M. Diehl, 'NAFLD, NASH and liver cancer,' Nat Rev Gastroenterol Hepatol, vol. 10, no. 11, pp. 656-65, Nov 2013. [4] X. Y. Duan, L. Zhang, J. G. Fan, and L. Qiao, 'NAFLD leads to liver cancer: do we have sufficient evidence?,' Cancer Lett, vol. 345, no. 2, pp. 230-4, Apr 10 2014. [5] G. C. Farrell, V. W. Wong, and S. Chitturi, 'NAFLD in Asia--as common and important as in the West,' Nat Rev Gastroenterol Hepatol, vol. 10, no. 5, pp. 307-18, May 2013. [6] G. Vernon, A. Baranova, and Z. M. Younossi, 'Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults,' Aliment Pharmacol Ther, vol. 34, no. 3, pp. 274-85, Aug 2011. [7] Y.-C. Lin, S.-C. Chou, P.-T. Huang, and H.-Y. Chiou, 'Risk Factors and Predictors of Non-Alcoholic Fatty Liver Disease in Taiwan,' Ann. Hepatol., vol. 10, no. 2, pp. 125-132, 2011. [8] D. R. Felix, F. Costenaro, C. B. Gottschall, and G. P. Coral, 'Non-alcoholic fatty liver disease (Nafld) in obese children- effect of refined carbohydrates in diet,' BMC Pediatr, vol. 16, no. 1, p. 187, Nov 15 2016. [9] C.-H. Chen et al., 'Prevalence and Risk Factors of Nonalcoholic Fatty Liver Disease in an Adult Population of Taiwan: Metabolic Significance of Nonalcoholic Fatty Liver Disease in Nonobese Adults,' J Clin Gastroenterol, vol. 40, 2006. [10] T.-H. Tung et al., 'Clinical correlation of nonalcoholic fatty liver disease in a Chinese taxi drivers population in Taiwan: Experience at a teaching hospital,' BMC Research Notes, vol. 4, no. 315, 2011. [11] M. Birjandi, S. M. Ayatollahi, S. Pourahmad, and A. R. Safarpour, 'Prediction and Diagnosis of Non-Alcoholic Fatty Liver Disease (NAFLD) and Identification of Its Associated Factors Using the Classification Tree Method,' Iran Red Crescent Med J, vol. 18, no. 11, p. e32858, Nov 2016. [12] G. C. Farrell, S. Chitturi, G. K. Lau, J. D. Sollano, and N. Asia-Pacific Working Party on, 'Guidelines for the assessment and management of non-alcoholic fatty liver disease in the Asia-Pacific region: executive summary,' J Gastroenterol Hepatol, vol. 22, no. 6, pp. 775-7, Jun 2007. [13] O. K. Yajima Y, Narui T, Abe R, Suzuki H, Ohtsuki M, 'Ultrasonographical diagnosis of fatty liver: significance of the liver-kidney contrast,' The Tohoku journal of experimental medicine, vol. 139, no. 1, pp. 43-50, 1983. [14] H. Kang, 'The prevention and handling of the missing data,' Korean J Anesthesiol, vol. 64, no. 5, pp. 402-6, May 2013. [15] A. P. Dempster, N. M. Laird, and D. B. Rubin, 'Maximum Likelihood from Incomplete Data via the EM Algorithm,' Journal of the Royal Statistical Society Series B Methodological, vol. 39, no. 1, pp. 1-38, 1977. [16] C. Drummond and R. C. Holte, 'C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling,' ICML, 2003. [17] T. M. Therneau, E. J. Atkinson, and M. Foundation, 'An Introduction to Recursive Partitioning Using the RPART Routines,' 2017. [18] D. H. Wolpert and W. G. Macready, 'No Free Lunch Theorems for Optimization,' IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67-82, 1997. [19] L. Breiman, 'RANDOM FORESTS,' Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. [20] V. Vapnik and O. Chapelle, 'Bounds on Error Expectation for Support Vector Machines,' Neural computation, vol. 12, no. 9, pp. 2013-2036, 2000. [21] B. Schölkopf, 'Learning with Kernels,' Journal of the Electrochemical Society, vol. 129, no. November, p. 2865, 2002. [22] T. G. Dietterich, 'Ensemble learning,' in The Handbook of Brain Theory and Neural Networks, vol. 1, 2002. [23] S. Murthy and S. Salzberg, 'Decision Tree Induction: How Effective is the Greedy Heuristic?,' in KDD-95, 1995, p. 6. [24] C. S. Hsu and J. H. Kao, 'Non-alcoholic fatty liver disease: an emerging liver disease in Taiwan,' J Formos Med Assoc, vol. 111, no. 10, pp. 527-35, Oct 2012. [25] C.-C. Fu, M.-C. Chen, Y.-M. Li, T.-T. Liu, and L.-Y. Wang, 'The Risk Factors for Ultrasound-diagnosed Non-alcoholic Fatty Liver Disease Among Adolescents,' Ann Acad Med Singapore, vol. 38, no. 1, pp. 15-21, 2009. [26] F.-Y. SHI, W.-F. GAO, E.-X. TAO, H.-Q. LIU, and S.-Z. WANG, 'Metabolic syndrome is a risk factor for nonalcoholic fatty liver disease: evidence from a confirmatory factor analysis and structural equation modeling,' European Review for Medical and Pharmacological Sciences, vol. 20, pp. 4313-4321, 2016. [27] A. L. Schneider, M. Lazo, E. Selvin, and J. M. Clark, 'Racial differences in nonalcoholic fatty liver disease in the U.S. population,' Obesity (Silver Spring), vol. 22, no. 1, pp. 292-9, Jan 2014. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20468 | - |
dc.description.abstract | 近年來隨著肥胖(症)以及代謝症候群的盛行,非酒精性脂肪肝病儼然成為國人的健康議題,值得注意的是肝癌在台灣十大癌症死因中排名第二,而非酒精性脂肪肝病就有可能導致肝癌。本研究的目標為利用分類樹(classification tree)判定非酒精性脂肪肝病之潛在危險因子,再藉由數種不同的機器學習方法建立學習系統去做分類,同時,我也會比較各個分類器(classifier)的表現,其中分類器包含最近鄰居法(k-nearest neighbor)、引導聚集法(bootstrap aggregating)、隨機森林(random forest),以及支持向量機(support vector machine)。從研究結果可以發現,男性的非酒精性脂肪肝病危險因子可能包含: 代謝症候群、身體質量指數、三酸甘油脂、總膽固醇、年齡、腰臀比、高密度脂蛋白,以及低密度脂蛋白,而在分類器的評估上,支持向量機的表現最好,準確度、敏感度,與特異度分別可達86.9%、90.0%,與81.0%。由此可以推論:將分類樹與支持向量機這兩種方法做結合,對於將男性是否為非酒精性脂肪肝病患者做正確分類是有發展潛力的。另外,此研究將提高國人對於定期健康檢查之重要性的意識,進而預防代謝相關疾病的發生,且在未來針對減少台灣非酒精性脂肪肝病病例的臨床決策必定有所助益。 | zh_TW |
dc.description.abstract | The prevalence of obesity and metabolic syndrome has led non-alcoholic fatty liver disease (NAFLD) to become a serious health concern during recent years. NAFLD may also lead to hepatoma, which has high mortality rate in Taiwan. The objective of the study is to identify the potential factors of NAFLD by a classification tree (CT) first and then apply machine learning methods to the health examination data to construct the learning system. The performance of several methods including the k-nearest neighbor (KNN), bootstrap aggregating (Bagging), random forest (RF), and support vector machine (SVM) will be compared in this work. I observe that metabolic syndrome, body mass index, triglyceride, total cholesterol, age, waist-to-hip ratio, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol may be the risk factors of NAFLD for males and the SVM classifier gave the best performance (86.9% accuracy, 90.0% sensitivity, and 81.0% specificity). I infer from the study that a combination of decision trees and SVM have the potential to classify NAFLD in males properly. This work can bring more awareness to the importance of regular health checkups to prevent metabolic diseases and aid in the clinical decision making for decreasing NAFLD in Taiwan in the future. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:49:45Z (GMT). No. of bitstreams: 1 ntu-106-R04h41007-1.pdf: 1300134 bytes, checksum: e83264a41aa006c815dd4ac873ec1adc (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 摘要 …………………………………………………………………….. ⅰ
Abstract …………………………………………………………………. ⅱ Content ……………………………………………………………….… ⅲ List of Figures ……...………………………………………………….... ⅴ List of Tables …………………………………………………………… ⅵ 1 Introduction ……………………………………………………….… 1 2 Literature reviews ………………………………………………….... 3 3 Research Design and Framework ………………………………….... 8 3.1 Study Materials ………………………………………………… 8 3.2 Variable description ………………………………………...… 12 4 Methods ………………………………………………………….… 16 4.1 Description of the classification system …………………......... 16 4.2 Variable selection model: classification tree (CT) ………….… 17 4.3 Classification models ……………………………………….… 20 4.3.1 Bootstrap aggregating (Bagging) and random forest (RF) ..... 20 4.3.2 K-nearest-neighbor (KNN) classifier ………………….…… 21 4.3.3 Support vector machine (SVM) …………………………..… 23 5 Results …………………………………………………………....... 27 5.1 Evaluation measurement ……………………………………… 27 5.2 Performances for male populations ………………………….... 29 5.2.1 Variable selection models ……………………………........ 29 5.2.2 Classification models ………………………………….…. 35 5.3 Performances for female populations ……………………..…... 40 5.3.1 Variable selection models …………...……………….…… 40 5.3.2 Classification models …………………………………..… 45 6 Discussion ………………………………………………….……… 51 7 Conclusion ……………………………………………………….... 55 Reference …………………………………………………………....… 56 | |
dc.language.iso | en | |
dc.title | 機器學習之應用: 台灣高科技產業族群非酒精性脂肪肝病之分類與其危險因子之探討 | zh_TW |
dc.title | Machine Learning Application: Classification of Non-Alcohol Fatty Liver Disease and its Risk Factors in Taiwanese High-Tech Industry Workers | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 徐治平(Jyh-Ping Hsu),熊誼芳(Yi-Fang Hsiung) | |
dc.subject.keyword | 非酒精性脂肪肝病,機器學習,分類樹,支持向量機, | zh_TW |
dc.subject.keyword | NAFLD,machine learning,classification tree,support vector machine, | en |
dc.relation.page | 60 | |
dc.identifier.doi | 10.6342/NTU201703346 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2017-08-17 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
顯示於系所單位: | 統計碩士學位學程 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-106-1.pdf 目前未授權公開取用 | 1.27 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。