本論文主要探討乳癌與C型肝炎疾病之智慧型學習系統之設計與實現，所開發自動分類器可用於檢測乳癌與C型肝炎發病率之檢測。在乳癌檢測方面，首先使用主成分分析來識別數據中有價值部分，進一步降低數據的維度，接著使用多層感知網絡方法提取數據中特徵，並設計網絡結構以探索與開發數據，可增加或減少數據之維度。因此，模型之建立是為了先探索然後開發數據。經過訓練和學習後，模型可以分離出具有代表性的屬性與特性，最後通過使用支持向量機的遷移學習技術將特徵數據用作分類器。另外，在C型肝炎檢測方面則提出結合隨機森林、邏輯回歸算法及人工蜂群演算法之串聯式兩階段智慧學習系統。策略驗證則是進行了50次10折蒙地卡羅交叉驗證法，並使用多種性能指標進行比較。所提模型可用於檢測C型肝炎發病之多類概率，從而提高相關治療之有效性。最後，模型驗證結果顯示本論文所提兩種智慧型學習系統均具可行性與實用性。

This dissertation proposes intelligent learning systems for breast cancer and hepatitis C diseases, respectively. The study is an automatic classifier for detecting the probability incidence of breast cancer and hepatitis C virus. The principal component analysis first identifies valuable parts of the data and further reduces the dimensions for predicting breast cancer disease. The multilayer perceptron network method is submitted to extract characteristics of the data. The network structure is designed to explore the examined data as the dimensions are increased or decreased. As such, the model is established first to explore and then investigate data. After training and learning, the models could segregate the representative attributes and numbers, and the characteristic data are then used as classifiers through transfer learning techniques using support vector machines. Moreover, in the aspect of HCV diseases, this dissertation proposes a cascade two-stage intelligent learning system, which combines the random forest, logistic regression, and artificial bee colony algorithms. After conducting a 10-fold Monte Carlo cross-validation 50 times, data from the recent pandemic are used to verify the proposed method. Various indicators are utilized to compare with the latest algorithms used in relevant fields to evaluate the quantitative results. All the simulation results indicate that the proposed model can detect the multiclass probabilities of HCV incidence, thereby improving the effectiveness of appropriate treatments. Finally, the validation results demonstrate the feasibility and effectiveness of these two intelligent learning systems.

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