膀胱癌(bladder cancer)為泌尿道腫瘤中最常見的癌症，根據資料統計全世界膀胱癌患者每年約新增25萬人，且約有12萬人死於膀胱癌。膀胱癌的死亡率雖不是很高，發生率卻頗高，尤其台灣西南沿海烏腳病流行地區，發生率甚至是台灣其他地區的十倍，因此膀胱癌的診斷及治療已是現代醫學及公共衛生上不容小覷的問題。膀胱癌診斷一般是透過細胞診斷學的方法來瞭解癌細胞變化情形，然而，此種檢驗法只能診斷已形成腫瘤之膀胱癌細胞，目前尚沒有一種檢驗方法，能夠事先預測膀胱細胞之病變。由於基因是檢測癌症之重要指標，因此本研究試圖用基因之變化來預測膀胱癌之發生與否，但癌細胞的基因相當複雜且呈現不規則之變化，因此若能建立一套智慧型電腦系統，幫助診斷及分析受測者之癌細胞變化情形，將可協助醫師做早期之診斷及治療。

　　由於癌症發展過程充滿複雜與多樣性，而統計方法常須在某種特定假設條件下運作，在應用上有其侷限性，且膀胱癌細胞株可收集到之個案數量相當少，並且其基因所轉譯之蛋白質產生量是呈非線性之排列，故臨床上很難做快速及準確的診斷。因此本研究應用整體擴散技術，將少量的膀胱癌細胞株基因組合變化歸納出邏輯性，擴增出大量的膀胱癌基因數據，並利用類神經網路系統之搜尋能力，透過啟發式的學習過程，將非線性之基因組合找出其相似之特質。另外，將整體擴散技術結合類神經網路與傳統類神經網路模式進行比較分析，發現整體擴散技術結合類神經網路之測試與訓練的結果，比一般傳統之類神經網路的診斷模式準確率更高。經由整體擴散技術結合類神經推論系統分析結果，在訓練資料從5筆增加到17筆時，所診斷出正常人及膀胱癌患者分類的準確率由81.67% 穩定成長提升至100%。因此，本研究希望利用整體擴散技術結合類神經網路的特性，用膀胱癌基因之變化作為膀胱癌早期診斷之工具，以及醫療照護人員的參考依據，進而增進醫療資源的有效運用，提高醫療服務品質。

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