隨著科技日新月異的進步，自我健康照護也日漸受到重視，民眾藉由定期接受健康檢查及篩檢瞭解自身狀況，若發現疾病徵兆能及早治療則能提高存活率。然而，目前乳癌預測之研究僅對乳房影像或病歷資料分別做評估，但根據醫學研究報告指出，乳癌的發生與兩者都有密切的關係。於是本論文則結合兩者以建立乳癌預測模型，藉此提高預測乳癌的準確率。其中，有關影像資料的處理，本論文利用高效能的快速且自適性二維經驗模態分解強化影像，有效的切割出乳腺，再萃取其多項特徵值。另，本論文亦利用關聯規則中前後鍵的限制去改進決策樹演算法，使其在挑選節點時能夠更有效率，以改進傳統決策樹樹枝過於繁雜的問題，不僅節省時間，且亦有效的提高乳癌預測模型的準確率。實驗結果顯示，利用改良的決策樹建立的乳癌預測模型達到約98%的預測正確率。

In recent years, with the rapid advances in science and technology, people have paid more attentions to self-health conditions by using health examination. The health examination can avoid people missing the best time of disease diagnosis and treatment. The medical records of patients and mammogram diagnosis are contributory factors of breast cancer. Instead of using medical records or mammogram apart, the proposed method combines features automatically extracted from mammograms and medical records of patients to build a breast cancer prediction model. In preprocessing step of imaging data, the proposed method uses fast and adaptive bidimensional empirical mode decomposition (FABEMD) to segment the mammograms for glandular tissue. After integrating imaging data and clinical data, the proposed method uses search constraints to select significant features. The proposed approach solves the problem of the traditional decision tree which has complicated branches, not only saves time but also effectively improves the accuracy of prediction model of breast cancer. Our method was applied to real dataset which consists of 579 patients, and the results show that the proposed method attains high accuracy of 98%.

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