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研究生:平靖翔
研究生(外文):PING, CHING-HSIANG
論文名稱:運用決策樹探討輔助器具評估之關鍵決策因子:以輪椅為例
論文名稱(外文):Using Decision Tree to Explore the Assistive Technology Assessments Crucial Factors: Cases of Wheelchairs
指導教授:方國定方國定引用關係
指導教授(外文):FANG, KWO-TING
口試委員:徐濟世楊聰仁
口試委員(外文):HSU, JIH-SHIHYANG, TSUNG-JEN
口試日期:2021-07-19
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系
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:2021
畢業學年度:109
語文別:中文
論文頁數:80
中文關鍵詞:決策樹機器學習輔具科技輪椅評估
外文關鍵詞:Decision TreeMachine LearningAssistive TechnologyWheelchairAssessment
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全球人口已逐漸邁入老年化的階段,許多慢性病與功能障礙的比率提升,導致身心障礙人口逐年劇增,輔具需求也隨之急遽上升。由於輔具類型眾多且複雜,需要經由專業的治療師進行評估,幫助個案找到適合的輔具。而根據過往研究,「輪椅」的評估需求占最大宗,因此本研究透過CART決策樹演算法從「輪椅及推車」診斷紀錄中,針對「移位型輪椅」以及「仰躺/空中傾倒型輪椅」找出5個重要評估特徵 (頭部狀況、年齡、骨盆狀況、認知能力以及判斷能力),並透過單純貝式分類法建立評估模型,經10折交叉驗證後得到準確率72%。最後,結合LINE BOT 讓個案或個案家屬針對題目自我評估,並透過評估模型給予初步的輪椅款式建議,後續評估人員可以參考此資訊,快速且準確地給予個案合適的輔具。
The world population has gradually entered the stage of aging, the rate of chronic diseases and, dysfunction have increased, result in surge in the number of people with disabilities, the demand of assistive device also increased rapidly. There are wide variety of assistive device and complex, thus users need a physical therapist to help them to find an appropriate assistive device. According to previous research, the need for wheelchair assessment is the largest, therefore this study uses CART Decision Trees to explore 5 imporant factors for “wheelchair and cart” assessment records (Head motion controlled, Age, Pelvis, Cognitive function, Judgement), use Navie Bayes Classifier to establish assessment model, which accuracy of 72% in 10-Fold Cross Vaalidation. Finally, the wheelchair users or users’ families can self-assessment from questions through the assessment model combines with LINE BOT to recommend type of wheelchair., and the physical therapist can refer this information to accurately give the user appropriate assistive devices.
摘要 i
ABSTRACT ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題 1
1.3 研究目的 2
第二章 文獻探討 3
2.1 高齡社會結構下之輔具評估需求 3
2.1.1 高齡社會結構 3
2.1.2 長期照顧需求 3
2.1.3 輔具科技 4
2.2 人工智慧 10
2.2.1 大數據概述 10
2.2.2 人工智慧概述 11
2.2.3 機器學習 12
2.2.4 深度學習 16
2.3 智慧醫療 17
2.3.1 機器學習於醫療照顧診斷 17
2.3.2 深度學習於醫療照顧應用 19
2.3.3 決策樹於醫療診斷應用 19
2.3.4 隨機森林於醫療診斷應用 21
第三章 研究方法 22
3.1 研究設計 22
3.2 資料搜集與準備 24
3.3 資料前處理 26
3.4 特徵選取 26
3.5 模型演算法 29
3.5.1 隨機森林 29
3.5.2 單純貝式分類法 31
3.6 績效評估方式 32
3.6.1 混淆矩陣 32
3.6.2 K折交叉驗證 33
第四章 研究分析與結果 34
4.1 資料蒐集與前處理 34
4.2 分析工具與套件 41
4.3 決策樹分析 42
4.3.1 樣本權重 43
4.3.2 最小節點樣本數 47
4.4 決策樹績效評估 51
4.5 分析結果解釋與說明 53
4.6 模組比較 54
4.7 使用者介面 57
第五章 結論與研究限制 62
5.1 結論 62
5.2 研究限制與建議 63
參考文獻 65
附錄 70


一、中文文獻
Jason Chen (2019年8月7日)。【機器學習】交叉驗證 Cross-Validation。 https://jason-chen-1992.weebly.com/home/-cross-validation

Tommy Huang(2018)。機器學習: Ensemble learning之Bagging、Boosting和AdaBoost。https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ensemble-learning%E4%B9%8Bbagging-boosting%E5%92%8Cadaboost-af031229ebc3

行政院新聞傳播處 (2020年2月3日)。長照2.0,照顧的長路上更安心。行政院。https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/dd4675bb-b78d-4bd8-8be5-e3d6a6558d8d

江婉琳 (2019)。運用決策樹探討病房型態選擇之研究: 以南部某區域醫院為例。國立中正大學資訊管理學系碩士在職專班碩士論文,嘉義縣。

林明潔 (2012)。應用決策樹於身心障礙者之電動輪椅評估。國立臺灣科技大學工業管理系碩士論文,台北市。

林淑玟 (2004)。輔助科技設備與服務之概論與相關法規介紹。http://sencir.spc.ntnu.edu.tw/site/c_file/a_download/t_key/3876
李淑貞 (2020年5月4日)。ICF簡介。衛生福利部社會及家庭署。

https://newrepat.sfaa.gov.tw/home/download
李淑貞, 余雨軒 (2011)。CNS 15390輔助科技分類技術手冊。內政部多功能輔具資源整合推廣中心,國立陽明大學ICF暨輔助科技研究中心。

周佩瑾, 紀彣宙, 陳信水, 謝文逸, 和 李旺澈. (2015). 依臨床復健觀點建置輔具處方決策支援系統-以輪椅應用為例. 台灣復健醫學雜誌, 43(1), 19-31.

施雅月、賴錦慧(譯)(2008)。資料探勘 (原作者:Pang-Ning Tan, Michael, Vipin Kumar)。台北市:台灣培生教育出版股份有限公司。(原著出版年:2006)

陳彥霖 (2013年4月2日)。淺談「新制身心障礙鑑定」制度。金門縣衛生局
https://phb.kinmen.gov.tw/cp.aspx?n=9C7ED050FF54B6F0

國家發展委員會 (2020年8月)。中華民國人口推估(2020至2070年)。國家發展委員會。https://pop-proj.ndc.gov.tw/download.aspx?uid=70&pid=70

衛生福利部 (2018 年 7月 3日)。長期照顧十年計畫2.0(106~115年)(核定本)。衛生福利部。https://1966.gov.tw/LTC/cp-4001-42414-201.html

衛生福利部社會及家庭署 (2020年10月13日)。108年度輔具服務彙整分析報告。衛生福利部社會及家庭署。https://newrepat.sfaa.gov.tw/home/download?conditions%5bcategory.id%5d=2c90e4c76705ab7f01671535b6e40d29

衛生福利部統計處 (2019年10月1日)。國際疾病分類標準(ICD-10)。衛生福利部。https://dep.mohw.gov.tw/dos/lp-2490-113-1-20.html

衛生福利部社會及家庭署 (2018年10月19日)。何謂輔助科技?。https://newrepat.sfaa.gov.tw/home/question/detail/2c90e4c76633b9e0016633c8cc0c26cc

二、英文文獻
Azar, A. T., Elshazly, H. I., Hassanien, A. E., & Elkorany, A. M. (2014). A Random Forest Classifier for Lymph Diseases. Computer Methods and Programs in Biomedicine, 113(2), 465-473.

Bangor, A., Kortum, P., & Miller, J. (2009). Determining What Individual SUS Scores Mean: Adding an Adjective Tating Scale. Journal of Usability Studies, 4(3), 114-123.

Belgiu, M., & Drăguţ, L. (2016). Random Forest in Remote Sensing: A Review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.

Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., & Popp, J. (2013). Sample Size Planning for Classification Models. Analytica Chimica Acta, 760, 25-33.

Bengio, Y., & Grandvalet, Y. (2004). No Unbiased Estimator of the Variance of K-Fold Cross-Validation. Journal of Machine Learning Research, 5, 1089-1105.

Bini, S. A. (2018). Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What do These Terms Mean and How Will They Impact Health Care ?. The Journal of Arthroplasty, 33(8), 2358-2361.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor ai: Predicting Clinical Events Via Recurrent Neural Networks. Machine Learning for Healthcare Conference, 56, 301-318.

Cook, A. M., & Polgar, J. M. (2014). Assistive Technologies-E-Book: Principles and Practice (4th Edition). Elsevier Health Sciences.
https://books.google.com.tw/books?hl=en&lr=&id=ODWaBQAAQBAJ&oi=fnd&pg=PP1&dq=Assistive+technologies:+Principles+and+practice&ots=IG13WvATX8&sig=lPOmL9-kr9lMGqXDViSNVx6tDKk&redir_esc=y#v=onepage&q&f=false

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big Data in Healthcare: Management, Snalysis and Guture Prospects. Journal of Big Data, 6(1), 1-25.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118. 

Ewert, T., Fuessl, M., Cieza, A., Andersen, C., Chatterji, S., Kostanjsek, N., & Stucki, G. (2004). Identification of the Most Common Patient Problems in Patients with Chronic Conditions Using the ICF Checklist. Journal of Rehabilitation Medicine, 36(1), 22-29.

Fatima, M., & Pasha, M. (2017). Survey of Machine Learning Algorithms for Disease Diagnostic. Journal of Intelligent Learning Systems and Applications, 9(01), 1-16.

Hashi, E. K., Zaman, M. S. U., & Hasan, M. R. (2017, February16-18). An Expert Clinical Decision Support System to Predict Disease Using Classification Techniques [Paper presentation]. 2017 International Conference on Electrical, Computer and Communication Engineering, Cox’s Bazar, Bangladesh.

He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.

Jain, D., & Singh, V. (2018). Feature Selection and Classification Systems for Chronic Disease Prediction: A review. Egyptian Informatics Journal, 19(3), 179-189.

Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255-260.

Leonardi, Matilde, Timothy J. Steiner, Ann T. Scher, and Richard B. Lipton. (2005) The Global Burden of Migraine: Measuring Disability in Headache Disorders with WHO's Classification of Functioning, Disability and Health (ICF). The Journal of Headache and Pain, 6(6), 429-440.

López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013). An Insight Into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics. Information Sciences, 250 ,113-141.

Maji, S., & Arora, S. (2019). Decision Tree Algorithms for Prediction of Heart Disease. Information and Communication Technology for Competitive Strategies,40, 447-454.

Murff, H. J., FitzHenry, F., Matheny, M. E., Gentry, N., Kotter, K. L., Crimin, K., ... & Speroff, T. (2011). Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing. JAMA, 306(8), 848-855.

Nguyen, C., Wang, Y., & Nguyen, H. N. (2013). Random Forest Classifier Combined with Feature Selection for Breast Cancer Diagnosis and Prognostic. Journal of Biomedical Science and Engineering, 6(5), 551-560.

Raghupathi, W., & Raghupathi, V. (2014). Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems, 2(1), 1-10.

Rodriguez, J. D., Perez, A., & Lozano, J. A. (2009). Sensitivity Analysis of K-Fold Cross Validation in Prediction Error Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 569-575.

Sarwar, A., & Sharma, V. (2012). Intelligent Naïve Bayes approach to Diagnose Diabetes Type-2. IJCA Special Issue on Issues and Challenges in Networking, Intelligence and Computing Technologies, 3, 14-16.

Sathyadevi, G. (2011, June3-5). Application of CART Algorithm in Hepatitis Disease Diagnosis [Paper presentaion]. 2011 International Conference on Recent Trends in Information Technology, MIT, Anna University, Chennai.

Saxena, K., & Sharma, R. (2015, June 3-5). Efficient Heart Disease Prediction System Using Decision Tree [Paper presentation]. International Conference on Computing, Communication & Automation, MIT, Anna University, Chennai.

Sharma, H., & Kumar, S. (2016). A Survey on Decision Tree Algorithms of Classification in Data Mining. International Journal of Science and Research, 5(4), 2094-2097.

S. Rasoul Safavian and David Landgrebe (1991). A Survey of Decision Tree Classifier Methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660-674.

United Nations. (2019). World population prospects 2019: Highlights. Department of Economic and Social Affairs, Population Division.

Verikas, A., Gelzinis, A., & Bacauskiene, M. (2011). Mining Data with Random Forests: A Survey and Results of New Tests. Pattern Recognition, 44(2), 330-349.

World Health Organization (WHO). (‎2001)‎. International classification of functioning, disability and health: ICF. World Health Organization. https://apps.who.int/iris/handle/10665/42407

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