在醫院的醫療資訊環境中不斷有各類型軟體專案的需求。能有效的預估軟體開發時程，對醫院的資訊單位在執行成效上有極大的影響，目前最大的問題在於評估軟體專案時，沒有相對應的一套評估流程，以適切的評估軟體的工時，以及人員的時程安排。因此找出一個有效的軟體時程預估之流程與作法，對醫院的資訊單位而言有其必要性。
本研究以南部某醫學中心資訊室為例，提出經驗式的評估工時的方法。首先收集以往的25筆專案資料，以此資料集為基礎，藉由功能點分析，以迴歸分析預測每個功能點數將需要撰寫的程式碼數量，作為COCOMO II的輸入參數。再以COCOMO II的估算工作量公式透過對數變換，取得案例單位的經驗參數，再以COCOMO II非線性迴歸模型配合專案的權重與成本因子，作專案時程的預估，在實作過程，我們發現若直接以簡單迴歸模式來預估，在檢驗迴歸模型時，會有解釋能力不足的問題，其原因為開發架構與人員的不同，造成使用功能點分析評估軟體複雜度時的差異。因此再以多元迴歸分析來發掘關鍵變數對於迴歸模型的預測影響，以改進預測解釋能力的問題。其次我們請4位資深同仁，就這些專案進行風險評估，我們將風險分為5個層級，再以資料探勘、類神經網路、決策樹等方式，根據專案風險與預估誤差之間的關聯性，修正相對應的誤差，讓預估更加準確，最後再加入11筆最新的專案資料作為驗證。目前相關研究只使用類神經網路及決策樹的方式，評估結果顯示，對於案例單位的專案資料集來說，資料探勘的修正方法，平均預估穩定度較類神經網路為高，未來將以此模型作為案例單位新專案開發之參考。

In this research, we propose a refined software cost prediction model based on the experiences of the department of medical informatics of a medical center in southern Taiwan. The model is an extension of COCOMO II. Firstly, we analyze 25 completed projects. We find that the single regression model is not good enough to interpret the measured data. We then introduce the multi-variable regression model. After deriving the cost model, we introduce the risk factor by the help of four senior staffs to define the risk levels of the projects. We divide the risk factor into 5 levels. Currently, related researches fine tune the results using decision tree and neural network approaches. We propose to use association rules (of data mining).
We use 11 newly developed projects to evaluate the proposed approach.The results show that association rules and neural network approaches would improve the accuracy of estimation model and they are better then the decision tree approach. The association rules approach shows 13% improvement of MMRE(Mean Magnitude of Relative Error), 35% improvement of PRED(Prediction level)(25%), while the neural network approach shows 14% improvement of MMRE, 31% improvement of PRED(25%). In comparison, association rules approach is better than neural network. Hence, we suggest to use the association rules approach in the future.

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