在醫院方面，醫生必須每天不斷的為病人診斷是否患有疾病。醫生有時候會在相同的案例中有不一樣的意見。為了能夠達成將所有醫生意見整合的目的，我們可以建立一個專家系統，這個專家系統是能夠學習專家知識跟個人經驗。換句話說，我們能夠輕鬆的使用專家的知識去做決策。這篇提出的演算法主要是希望能夠去幫助醫生做出高準確率的決策。
本篇所提出的專家系統主要架構是來自於自適應神經網路模糊推論系統 (ANFIS)。 ANFIS就像是一位專家。首先，我們會針對各個案例的屬性進行分類並決定出ANFIS的使用個數。ANFIS會產生許多結果。最後，我們利用投票法做出做決策，而不同的意見會在投票法中被整合。我們利用本篇提出的演算法應用在三個不同案例上面分別是(1)乳癌診斷、(2)肝炎診斷和(3)信用審查，而它們的準確率分別可以達到95.62%, 87.1% 和 84.6%。

In the hospital, the doctors have to diagnose patients whether they get disease or not. Sometimes, doctors may have different opinions in the same case. In order to integrate all of the opinions, we build an expert system to learn expert acknowledge and personal experience. On the other hand, we can use this experts’ knowledge in the decisions making easily. This paper proposed a method helps doctors make an accurate decision in many fields.
This study proposed an expert system based on the adaptive network based fuzzy inference system algorithm. First, the system begin with classifying the attribute types and deciding the number of ANFISs according to the databases. Next, the ANFISs serve as experts to provide diagnosis individually. Finally, the system takes a vote from those experts and makes a final decision. In this step, the different opinions are integrated into the voting process. The proposed expert system was used in the following three cases: (1) Breast Cancer Wisconsin, (2) Hepatitis and (3) Credit Approval. The accuracies of these three cases were 95.62%, 87.1%, and 84.6%, respectively.

REFERENCES
Amman, & Jordan (2011). Artificial Neural Networks in Medical Diagnosis, International Journal of Computer Science Issues, 8(2), 150-154.
Chun-Lung Chena, Chuen-Lung Chen (2009), A bottleneck-based heuristic for minimizing makespan in a flexible flowline with unrelated parallel machines. Computers & Operations Research, 36, 3073-3081.
Cyril Voyant, Marc Muselli, Christophe Paoli, & Marie-Laure Nivet (2013). Hybrid methodology for hourly global radiation forecasting in Mediterranean area. Renewable Energy, 53, 1-11.
C.C. Lee (1990). Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part II,. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 419-435.
D. Radojevic, &S. Petrovic (1997). A fuzzy approach to preference structure in multicriteria ranking. International Transactions in Operational Research, 4(5), 419-430.
E.H. Mamdani , & S. Assilian (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
Fatma Taher, Naoufel Werghi, Hussain Al-Ahmad, & Rachid Sammouda (2012). Lung Cancer Detection by Using Artificial Neural Network and Fuzzy Clustering Methods. American Journal of Biomedical Engineering, 2(3), 136-142.
Hussein A. Abbass (2010), An Evolutionary Artificial Neural Networks Approach for Breast Cancer Diagnosis, Knowledge Discovery and Data Mining, 10, 593-596.
Hogg, McKean, & Craig (1995), Mathematical Statistics. Sixth Edition, Printed in the United States of America. ISBN:0-13-122-605-3.
Jang, Jyh-Shing Roger, Sun, & Chuen-Tsai, (2009). Neuro-fuzzy modeling and control, Proceedings of the IEEE, 83,1-29.
Jang, Shing-Jhy Roger, (1993). ANFIS: Adaptive-Network-based Fuzzy Inference System. Transcation on system Man, and Cybernetics, 23(3), 665-684.
Jason, S.M (1954), Optimal two-and three-stage production schedules with setup times included. Naval Researcher Logistics Quarterly, 1, 61-68.
J. Brito1, F.J. Martinez1, Jose A. Moreno-Perez 1, & J.L. Verdegay (2012). ACO-GRASP-VNS Metaheuristic for VRP with Fuzzy Windows Time Constraints. Lecture Notes in Computer Science, 6927(1), 440-447.
J. Brito1, F.J. Martinez, J.A. Moreno1, & J.L. Verdegay (2009), A GRASP---VNS Hybrid for the Fuzzy Vehicle Routing Problem with Time Windows. IEEE International Conference, 1, 825-832.
Kemal Kilic, Beth A. Sproule, I. Burhan Turksen, & Claudio A. Naranjo (2004). A fuzzy system modeling algorithm for data analysis and approximate reasoning. Robotics and Autonomous Systems, 49, 173–180.
Kuang C. Wei, Gary A. Dage (1995). An Intelligent Automotive Climate Control System. Systems, Man and Cybernetics, Intelligent Systems for the 21st Century. IEEE International Conference , 4, 1-14.
L.A.Zadeh (1965). Fuzzy Set. Information and Control, 8, 338-353.
L.A. Zadeh (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Systems, Man, and Cybernetics, 3(1),28-44.
Li-Xin Wang (2010), A Course in the Fuzzy System and Control, First Edition.Pearson Education Taiwan Ltd. ISBN:978-986-154-130-3.
M. Pourmahyabadi, & S. Mohammad Nejad, (2009). Prediction of photonic crystal fiber characteristics by Neuro–Fuzzy system. Optics Communications, 282, 4081-4086.
Mi-Yi Kim, & YoungHoonLee, (2012). MIP models and hybrid algorithm for minimizing the makespan of parallel machines scheduling problem with a single server. Computers &Operations Research, 39 , 2457-2468.
Mary E. Kurza, & Ronald G. Askin (2003). Comparing scheduling rules for flexible flow lines. Int. J. Production Economics, 85, 371-388.
Maher Hamdi, Gerard Lachiver, & F. Michaud (1999). A new predictive thermal sensation index of human response, Energy and Buildings, 29(2), 167–178.
Mehdi.Neshat, & Mehdi.Yaghobi (2009), Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System. Proceedings of the World Congress on Engineering and Computer Science, 1, 1-6.
Nesma Settouti, M. Amine Chikh , & Meryem Saidi (2012), Generating fuzzy rules for constructing interpretable classifier of diabetes disease, Australas Phys Eng Sci Med, 39(3), 257-270.
Oscar .C, & Arnaud Quirin (2010). Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection, International Journal of Hybrid Intelligent Systems, 7(1), 45–64.
Papagelis A., & Kalles D.(2001). Breeding Decision Trees Using Evolutionary Techniques, Proceedings of the Eighteenth International Conference on Machine Learning, 393-400.
Rogers. S, Fiechter .C, & Langley, P. (1999). An adaptive interactive agent for route advice. Thirs Int. Conf. on Autonomous Agents, Seattle, 198-205.
Sepideh Karimi, OzgurKisi, JalalShiri, & OlegMakarynskyy (2013), Neuro-fuzzy and neural network techniques for forecasting sea level in Darw in Harbor, Australia. 50, 52-59.
S. Jassar, Z. Liao, & L. Zhao (2009). Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems, Building and Environment, 44, 1609–1616.
Shi, Yang, &Wang, Xiaojing (1996). ITS in China: Backgrounds and Prospects, IEEE Vehicle Navigation and Information Systems Conference, 7, 132-138.
Thorsten H. Ecke, Steffen Hallmann, Stefan Koch, Jurgen Ruttloff, Henning Cammann, Holger Gerullis, KurtMiller,& Carsten Stephan (2012). External Validation of an Artificial Neural Network and Two Nomograms for Prostate Cancer Detection, International Scholarly Research Network, Hindawi Publishing Corporation, 8, 1-6.
Thomas L. Saaty (2008). Decision making with the analytic hierarchy process. scientific and professional journals, 1(1), 83-98.
T. Takagi and M. Sugeno, Derivation of fuzzy control rules from human operator’s control actions. Fuzzy Inform., Knowledge Representation and Decision Analysis, 4, 55-60.
V. KAMINSKAS, & R.LIUTKEVICIUS (2005), Learning Fuzzy Control of Nonlinear Processes. 16(4), 571-586.
Zhicheng Li, Huijun Gao, & Ramesh K. Agarwal (2013), Stability analysis and controller synthesis for discrete-time delayed fuzzy systems via small gain theorem. Information Sciences , 226, 93-104.
Zoran. M, Marko.M, Mihailo. L, & Bojan. B (2013). Neural network Reinforcement Learning for visual control of robot manipulators. Expert Systems with Applications, 40(5), 1721–1736.