台灣是一個慢性肝炎疾病流行的地區。在民國七十年初期，肝癌在台灣已成為引起死亡率的癌症中排名第一。此外，肝硬化和慢性肝臟疾病是死亡原因排名的第六與第七。這嚴重的疾病影響國人健康，並且也帶來許多醫療成本。本研究提出直覺模糊神經網路，藉由急性肝炎病患在急診室的診療結果，進而發展預測病患醫療費用之預測模型。本研究改良在模糊神經網路中的模糊邏輯 (fuzzy logic, FL) 僅考慮歸屬函數值 (degree of membership function)，之中尚存許多不確定性未考慮。而直覺模糊邏輯 (intuitionistic fuzzy logic, IFL) 的優點在於同時考慮歸屬函數值與非歸屬函數值 (degree of non-membership function)，進而發展出猶疑度 (degree of hesitation) 的概念，使歸屬函數不能考慮到的不確定性獲得完整的定義。考慮猶疑度後使得不確定區域減少，使模糊神經網路的預測效能大幅改善。在本研究中，發展兩個演算法來最佳化直覺模糊類神經網路中的參數，包含倒傳遞演算法 (back-propagation algorithm) 與基因演算法 (genetic algorithm)。此外，在本研究中應用其所發展之直覺模糊類神經網路建構急性肝炎病患的醫療成本之預測模型，在經過與模糊神經網路 (fuzzy neural network, FNN)、類神經網路 (artificial neural network, ANN) 與支援向量迴歸 (support vector regression, SVR) 所建構之預測模型結果比較，得知本研究所發展之直覺模糊類神經網路有較好的預測效能，進而有效幫助使用者 (醫師或護理人員) 提早作醫院資源的規劃，達到有限資源做最有效率的運用。

Taiwan is an endemic area for chronic hepatitis disease. Since the early 1980’s, liver cancer has become the first cancer mortality causes among other cancers in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth rank and seventh rank in the causes of death, respectively. This is a serious disease affecting people's health, and it brings a lot of medical cost as well. This study develops a medical cost forecasting model for the acute hepatitis patients in the emergency room. In order to consider the uncertainty and hesitation in the human being’s thinking, this study employs the intuitionistic fuzzy logic (IFL) since it considers membership, non-membership, and hesitation values simultaneously. The proposed model combines the intuitionistic fuzzy neural network (IFNN) with Gaussian membership function and Yager-Generating function to enhance the performance of FNN. Furthermore, a back-propagation learning algorithm and genetic algorithm (GA) are applied in order to optimize the parameters and weights of the proposed IFNN. The proposed IFNN is applied to solve ten benchmark datasets including the nonlinear control and prediction problems. The computational results showed that the IFNN is more efficient than conventional algorithms, such as an artificial neural network (ANN), a fuzzy neural network (FNN), and a support vector regression (SVR). In addition, GA-based IFNN outperforms IFNN with back-propagation learning algorithm in forecasting accuracy. In the real-world problem, the proposed method can really support physicians in planning medical resources and make a good decision to make the most efficient use of limited resources.

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