臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：運用基因演算法建構疾病預測模型之研究-以尿路結石疾病預測為例
作者：江宏志
校院名稱：國立臺灣大學
系所名稱：商學研究所
指導教授：蔣明晃
學位類別：博士
出版日期：2003
主題關鍵詞：人工智慧；基因演算法；疾病預測模型
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:43
運用電腦輔助醫師做診斷的工作一直是醫生或研究生命科學的學者所致力的方向。當臨床醫師遇到病患有眾多的數據資料需要分析時，很難用自己的記憶及個人大腦的判斷來診斷疾病，僅能將繁雜的病患資訊一一分開判讀，而無法做到資訊整合分析的工作。由於現今的電腦可以處理大量而且複雜的資訊，所以如何運用電腦的強大運算能力來做病患資訊整理與分析是大部份醫師目前所傾向努力的一個方向。電腦運算中的人工智慧技術可以補強現今醫師對於病患資料分析之不足部分，透過電腦的模擬運算，可以整合各式各樣的臨床資料與實驗數據，以期得到較佳的判讀結果。
本研究運用基因演算法的人工智慧建模工具建置出一個應用在尿路結石疾病的預測模型。此模型除了能考慮尿路結石相關基因的基本資料之外，對於病患的飲食習慣、運動習性及其他相關的資訊亦能一併的考慮到。除此之外，本研究也針對同一批病患的資訊，分別使用其他預測建模的技術 (如鑑別分析、邏輯迴歸、決策樹與類神經網路) 去建置出疾病預測模型以比較各類型的建模技術與基因演算法模型的優劣。為了能驗證基因演算法模型的有效性，我們在研究中還採用了田口實驗的設計技巧做一連串的的實驗。而所使用的模型效益評估指標有五個：病患分類正確率、分類敏感度、分類鑑別度、預測得病之概似比值與預測健康之概似比值。若將此基因演算法模型與其他四種預測模型相互比較，可以發現到基因演算法預測模型在這五個評估指標中都得到了相當良好的表現。本研究可說是極少數能夠整合國人之尿路結石相關的遺傳基因多型性的資訊與人工智慧的技術然後應用在疾病預測之研究，而且得到相當好的疾病預測結果。由此可知此基因演算法預測模型確實可以有效的應用在尿路結石疾病病患的預測分類問題上面，同時也證明人工智慧在疾病的預測應用上是相當可行而且值得期待的。
Using computer-aided tools when diagnosing patients is a good research method for doctors and researchers in life science these days. When clinical doctors are handling with different kinds of patients’ data these days, it’s quite difficult for them to use personal judgment in diagnosing what kind of disease the patient might have. The doctors can only use individual data analysis and basic statistics to find clues or patterns from patients’ raw data. Because computers nowadays have the ability to deal with mass and complex information, the doctors are also trying to utilize the computer’s powerful computing ability to analyze and manage patients’ information. Artificial Intelligence computing tools are able to complement what doctors lack of these days, they are able to combine different kinds of clinical patient data from the patients’ database and acquire much more readable results from the raw data.
This study uses Genetic Algorithm to construct a disease predictive model for urinary stone disease. The model not only utilizes patients’ gene information related in stone disease, but also considers patients’ other environmental information such as daily eating habits and exercise habits. Besides constructing the Genetic Algorithm model, this study also uses different kinds of predictive model techniques (such as Discriminate Analysis, Logistic Regression, Decision Tree and Artificial Neural Network) to construct the same disease predictive model. The reason for doing this is because we want to compare our Genetic Algorithm model’s predictive accuracy with other predictive model techniques. In order to acquire objective conclusion for the comparison study, we also use Taguchi experiment technique to setup an experiment for our analysis models. There are five evaluation indexes we have used when comparing the models as below: patients’ classification rate, sensitivity, specificity, positive predictive value and negative predictive value.
From the results of the experiment, we found out that the Genetic Algorithm model has good performance in five evaluation indexes. This study is one of the very few studies before that is able to use patients’ gene information and Genetic Algorithm technique to construct a disease predictive model in Taiwan. Based on this study, we have shown that the disease predictive model using Genetic Algorithm is able to implement quite successfully on urinary stone disease. This study also has shown that artificial intelligence predictive tools used on disease prediction are quite suitable and possible in the future.
Keywords: artificial intelligence, genetic algorithm, disease predictive model
一、中文部分
李輝煌，民89。田口方法，高立圖書有限公司。
吳國禎，民89。資料探索在醫學資料庫之應用，中原大學醫學工程研究所未出版碩士論文。
林文修，民89。演化式類神經網路為基底的企業危機診斷模型：智慧資本的應用，中央大學資訊管理所未出版博士論文。new window
周文賢，民91。多變量分析，智勝文化出版社。
梁定澎，民81。決策支援系統，松崗出版社。
曾君俊、朱唯勤、詹寶珠、鍾文裕、潘宏基，民87。以類神經網路輔助加馬刀立體定位放射手術治療計畫之初步研究，中華醫學工程期刊，第18卷第二期，97-96。
陽銘燿、徐良育、胡威志、張恆雄、高材著，民86。利用小波轉換與類神經網路進行心電圖特徵擷取與病症分類 ，中華醫學工程期刊，第17卷第四期， 256-266。
葉怡成，民90。類神經網路模式應用與實作，儒林書局。
二、英文部分
Adriaans, P. and Zantige D.(1996). Data Mining, Adison-Wesley.
Avent, R., Chong N. and Neal J. A.(1994). Machine vision recognition of facial affect using backpropagation neural network. Engineering in Medicine and Biology Society, vol.2 , pp. 1364-1365.
Azimi, Sadjadi, Newman M. R. and Zeligman F. (1997). Detection of clusters of microcalcifications using neural network-based schemes. Engineering in Medicine and Biology Society , pp.533-534.
Baxt, W.G..(1990). Use an artificial neural network for data analysis in clinical decision making: the diagnosis for acute coronary occlusion. Neural Computation, vol. 2, pp.480-489.
Berry, M. J. and Linoff G..(1997). Data Mining Techniques for Marketing, Sales, and Customer Support, New York: John Wiley.
Breiman, L., Friedman J. H., Olshen R. and Stone C.(1984). Classification and Regression Trees, CA: Wadsworth and Brooks.
Cabena, P., Hadjinian P., Stadler R., Verhees J. and Zanasi A.(1998). Discovering Data Mining: From Concept to Implementation. NJ: Prentice Hall.
Chiang, David, Chiang H.C., Chen W.C. and Tsai F.J.(2003). Prediction of stone disease by using discriminate analysis and artificial neural networks in genetic polymorphisms- A new modality. British Journal of Urology. (In Press)
Chen, W.C., Chen H.Y., Lu H.F., Hsu C.D. and Tsai F.J.(2001). Association of vitamin D receptor gene start codon Fok I polymorphism with calcium oxalate stone disease. British Journal of Urology, vol. 87, pp.168-71.
Chen, W.C., Chen H.Y., Wu J.Y., Chen Y.T. and Tsai F.J.(2002). Osteocalcin gene Hind III polymorphism is not correlated with calcium oxalate stone disease. Urology Research. (In Press)
Chen, M.S., Han J. and Yu S. P.(1996). Data Mining: An overview from a database perspective. IEEE Transactions On Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883.
Chen, W.C., Chiang D., Yang S. J. and Chiang H.C.(2003). Use of genetic algorithms and artificial neural network to the prediction of stone disease from clinical parameters and genetic polymorphisms. Urology. (In Press)
Cios, K.J., Chen K. and Langenderfer R.A.(1990). Use of neural network in detecting cardiac disease from Echocardiographic images. IEEE Engineering in Medicine and Biology Magazine, pp. 58-60.
Clark, P. and Niblett T.(1989). The CN2 induction algorithm. Machine Learning, vol. 3, no. 4, pp. 261-283.
Coe , F.L., Keck J. and Norton E.R.(1977). The natural history of calcium urolithiasis. JAMA, vol.238, pp.1519-1523.
Curhan, G..C., Willett W.C., Rimm E.B., Spiegelman D. and Stampfer M.J.(1996). Prospective study of beverage use and the risk of kidney stones. Amsterdam Journal of Epidemiol , vol.143, pp. 240-7.
Dahr, V. and Stein R.(1997). Seven Methods for Transforming Corporate Data into Business Intelligence, NJ: Prentice Hall.
Edward, Hance. (1976). Computer-based Medical Consultations:MYCIN, Elsevier Scientific Publishing Corporation.
Edward, Hance. (1993). The adolescence of AI in medicine: will the field come of age in the 90’s? Artificial Intelligence in Medicine, vol. 5, pp. 93-106.
Eldon, Y.L.(1994). Artificial neural networks and their business applications. Information and Management, vol. 27, pp.303-313.
Englehart, K., Hudgins B., Stevenson M. and Parker P.A.(1995). A dynamic feedforward neural network for subset classification of myoelectric signal patterns. Engineering in Medicine and Biology Society, vol. 1, pp.819-820.
Evolver Software(1998), http://www.palisade.com.
Fayyad, Usama, Piatetsky-Shapiro G., Smyth P. and Uthurusamy R.(1996a). Advances in knowledge discovery and data mining. CA:AAAI Press/MIT Press.
Fayyad, Usama, Haussler D. and Stolorz P.(1996b). The data warehouse and data mining. Communication of the ACM, vol. 39, no. 11 , pp. 49-57.
Fisher, R.A.(1936).The use of multiple measurements in taxonomic problems. Annals of Eugenics, vol.7, pp.179-188.
Fletcher, Robert H., Suzanne W. F. and Edward H. W.(1985). Clinical Epidemiology: the Essentials, Addison-Wesley.
Floyed, C.E. ,Jr. (1991). An artifical neural network for SPECT image reconstruction. Medical Image, vol.10, pp. 485-487.
Fujita, H. , Katafuchi T., Uehara T. and Nishimura T.(1992). Neural network approach for the computer-aided diagnosis of coronary artery diseases in nuclear medicine. International Joint Conference on Neural Network, vol.1, no. 3, pp. 215-220.
Goddard, J. , Lopez I. and Rufiner H. L.(1991). Improving the classification accuracy of cardiac patients. Engineering in Medicine and Biology, vol.10, pp. 485-487.
Greene, D. P. and Smith S.F.(1987). A genetic system for learning models of consumer choice. Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms. NJ: L.Erlbaum Associates, pp. 45-58.
Greenhalgh, T.(1997). How to read a paper-The basics of evidence based medicine. BMJ Publishing Group, pp. 100-111.
Groth, R.(1998). Data Mining : A Hands-On Approach for Business Professionals. NJ: Prentice Hall.
Hand, D.J.(1981). Dicrimination and Classification New York: John Wiley and Sons.
Harrison, R.F., Marshall S.J. and Kennedy R.L.(1991). The early diagnosis of heart attacks: A neurocomputational approach. IJCNN, vol.9, no. 1, pp. 1-5.
Hillis, D.W. (1999). The Pattern On the Stone : The Simple Ideas That Make Computers Work (Science Masters Series). Perseus Books.
Hiraiwa, A. , Shimohara K. and Tokunaga Y.(1990). EGG topography recognition by neural network. IEEE Engineering in Medicine and Biology Magazine, pp. 39-42.
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press.
Holland, J.H. (1995) Hidden Order: How Adaptation Builds Complexity. Addison-Wesley.
Hopefield, J. and Tank D.(1985). Neural computations of decisions in optimization problems. Biological Cybernetics, vol. 51, pp. 141-152.
Hojker, S., Kononenko I., Jauk A., Filder V. and Porenta M.(1988). Expert system’s development in the management of thyroid diseases. Proc. European Congress for Nuclear Medicine, pp. 58-72.
Kenneth, G. M., Max A.W. and Stallard E.(1988). Models of the interaction of mortality and the evolution of risk factor distribution. Statistics in Medicine, vol.7, pp.239-256.
Koehler, G.J.(1991). Linear discriminate functions determined through genetic search. ORSA Journal of Computing, vol.3, no.4, pp. 345-357.
Kononenko, I.(1993). Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence, vol. 7, pp. 317-337.
Kononenko, I., Bratko I. and Roskar E.(1984). Expert system in automatic learning of medical diagnostic rules. International School for the Synthesis of Expert’s Knowledge Workshop, pp. 120-135.
Korenberg, M. J. and Morin, E. L. (1995). Parallel cascade classification of myoelectric signals. Engineering in Medicine and Biology Society, vol. 2, pp. 1399-1400.
Kukar, M., Kononenko I. and Silvester T.(1996). Machine learning in prognosis of the femoral neck fracture recovery. Artificial Intelligence In Medicine, Vol.8, pp. 431-451.
Lee ,Y.H., Huang W.C., Tsai JY, et al.(2002). Epidemiological studies on the prevalence of upper urinary calculi in Taiwan. Urology International, vol. 68, pp.172-177.
Luciano, J. S, Cohen M. A. and Samson J. A.(1970). Predictive medicine:initial symptoms may determine outcome in clinically treat depressions. Neural Network, vol.1, pp. 71-80.
Mao, Jain, M.(1996). Artificial neural networks: A tutorial. IEEE , pp.5-15.
McAvoy, T., Su H. and Werbos P.(1992). Long-term predictions of chemical processes using recurrent neural networks: A parrallel training approach. Industry Engineering Chemical Research, vol. 31, no. 5, pp. 1338-1351.
McClelland, J. L., Rumelhart D. E. and Hinton G. E.(1986). The appeal of parallel distributed processing. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, no. 1, pp. 3-44.
McCulloch, W.S. and Pitts, W.(1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathemactical Biophysics, vol. 5., pp. 115-137.
Michalewicz, Z. (1994). Genetic algorithms + Data Structures=Evolution programs(2nd edition). London:Springer-Verlag.
Minami, K. , Ohkuma Y., Nakajima H. and Toyoshima T.(1996). Arrhythmia diagnosis system which can distinguish atrial rhythms from ventricular rhythms. Engineering in Medicine and Biology, vol. 4, pp. 1640-1641.
Negnevitsky, M.(2002). Artificial Intelligence- A Guide to Intelligent System, Addison Wesley.
Nicholas, V. F. (1991). An Artificial Intelligence Technique for Information and Fact Retrieval- An Application in Medical Knowledge Processing. MIT Press.
Office2000 Software(2000), http://www.microsoft.com.
Pacific Northwest National Laboratory.(1996). Neural networks applications in medicine and health. http://www.emsl.pnl.
Parfitt, C. M. , F. S. Mikelberg and N. V. Swindale(1995). The detection of Glaucoma using an artificial neural network. Engineering in Medicine and Biology Society, pp. 847-848.
Quinlan, J.R.(1986). Induction of decision trees. Machine Learning, vol. 1, no.1, pp.81-106
Quinlan, J.R.(1993). C4.5 Programs for Machine Learning. CA: Morgan Kaufmann.
Roskar, E., Abrams P., Bratko I., Kononenko I. and Varsek A.(1986). Cuds-An expert system for the diagnostics of lower urinary tract disorders. Journal of Biomedical Measurements, Informatics and Control, vol.1, no. 4, pp. 201-204.
Schatzkin, Arthur, Cupples Adrienne and Heeren Tim.(1984). Sudden death in the Framingham heart study- differences in incidence and risk factors by sex and coronary disease status. American J. of Epidemiology, vol. 120, No. 6, pp.888-899.
Shapiro, Piatesky, G.regory and Frawley W.J.(1991). Knowledge Discovery in DataBases. CA:AAAI Press/MIT Press.
Siddhartha, Bhattacharyya and Pendharkar C.(1998). Inductive, evolutionary and neural computing techniques for discrimination: A comparative study. Decision Sciences, vol 29, no. 4, pp.45-60.
SPSS Data Mining.(1998). http://www.spss.com/datamine/whitepap.htm.
Syiam, M. M.(1994). A neural network expert system for diagnosing eye diseases. Proceedings of the Tenth Conference on Artifical Intelligence for Application, pp. 491-492.
Syswerda, G. (1989). Uniform crossover in genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms, pp.2-9.
Szolovits, Peter and Stephen G. P.(1984). Categorical and Probabilistic Reasoning in Medical Diagnosis. Readings in Medical Intelligence- The First Decade. Addison-Wesley, pp. 56-78.
Taguchi, G.., Konishi S. and Wu Y.(1992). Taguchi methods, research and development. Quality Engineering Series, vol. 1, pp. 15-25.
Taguchi, G.., Yokoyama Y. and Wu Y.(1993). Taguchi methods, design of experiments. Quality Engineering Series, vol. 4, pp. 35-48.
Taguchi, G.., Yokoyama Y. and Wu Y.(1994). Taguchi methods, on-line production. Quality Engineering Series, vol. 2, pp. 56-80.
Tsai, F.J., Chen H.Y., Lu H.F., Hsu C.D. and Chen W.C.(2002a). Urokinase gene 3’-UTR C/T polymorphism is associated with Urolithiasis. Urology, vol. 59, pp. 458-61.
Tsai, F.J., Wu H.C., Chen H.Y., Lu H.F., Hsu C.D. and Chen W.C. (2002b). Association of E-Cadherin gene 3''-UTR C/T polymorphism with calcium oxalate stone disease. Urology International, 2002b, (In Press).
Van, Gils, M. , Jansen H., Nieminen K., Summers R. and Weller P. R.(1997). Using artifical neural networks for classifying ICU patient states. IEEE Engineering in Medicine and Biology Magazine, vol. 16, pp. 41-47.
Wang, D. C. and Juni J. E.(1992). Artifical neural network nuclear cardiac stress test interpretation. Nuclear Science Symposium and Medical Imagine Conference, vol. 2, pp. 1282-1284.
Wardrop, M. Mitchell.(1994). Complexity-the Emerging Science at the Edge of Order and Chaos, Chinese Translation Copyright by Common Wealth Publishing Corporation.
Welstead, S. T.(1994) Neural Network and Fuzzy Logic Applications in C/C++, John Wiley and Sons.
Yoon, Y.(1990). A connectionist expert system for dermatology diagnosis. Expert Systems, pp. 23-31.
Zhao, S. X. and Taiwei Lu.(1995). The classification of the depth of burn Injury using hybrid neural network. Engineering in Medicine and Biology Society, vol.1, pp. 815-816.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
:::
無相關書籍
 
無相關著作
 
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：837（臺灣：823，外國：14） ／ 本年度總使用人次：1972506 ／ 訪客人次（自102年09月）：67776413
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上