改良式 ARIMA演算法預測台灣未來五年之癌症死亡率
莊政宏、 呂威甫、陳瑞奇
10.6283/JOCSG.201912_7(4).294
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中文摘要 近年來，65 歲以上癌症死亡數占比呈上升趨勢。死因之統計與分析有助於公共衛生政策之規劃與全民健康之提升。趨勢延伸演算法（時間序列分析）常被用於疾病定量預測，其中之一就是自迴歸整合移動平均(ARIMA)模型，該模型可以用來對時間序列資料進行預測，尤其針對隨機過程特徵隨時間變化、而且導致時間序列非平穩的原因是隨機的問題上特別有用。目前台灣各縣市歷年癌症死亡統計資料大都屬於所謂成因不明確、非平穩的時間序列資料集，適合使用趨勢延伸演算法對未來癌症死亡趨勢執行預測。本研究為了進一步提升預測的準確性，對傳統 ARIMA 演算法進行改良，我們先將截至目前為止衛福部已公布的台灣各縣市 1992 年至 2017 年共計 26 年癌症死亡率，分為訓練及測試資料，實施幾種不同方法的預測效能比較，其中一項是以平均絕對百分比誤差(MAPE)評估各方法之預測準確度，最後以最佳改良式 ARIMA 演算法預估台灣未來五年之癌症死亡率，提供政府相關單位事先了解癌症死亡的可能趨勢及其政策規劃上的參考，以便民眾（尤其是銀髮族）擁有適當的癌症篩檢機制、罹癌者都能獲得妥善治療，降低癌症死亡率，並提高生活品質。
關鍵字：銀髮健康、癌症死亡率、趨勢延伸演算法、ARIMA模型
文章建立時間：2020-01-13
引用格式(APA)：
莊政宏、 呂威甫、陳瑞奇(2020)。 改良式 ARIMA演算法預測台灣未來五年之癌症死亡率。
福祉科技與服務管理學刊， 7(4)， 294-309。
Using an Improved ARIMA Algorithm to Forecast the Cancer Mortality in Taiwan for the Next Five Years
Chuang, C.-H., Lu, W.-F., Chen, J.-C.
English Abstract The first cause of death in the elderly is also cancer, and unfortunately, the percentage of such death in people over 65 years old is increasing with days. The statistics and analysis of the cause of death have become significant for planning of public health policies and improving the overall health of the people of Taiwan. Trend extension algorithm is often used for quantitative disease forecasting. One such model is the Autoregressive Integrated Moving Average (ARIMA) model. This model can be used to forecast time series data, especially for problems where random process characteristics change over time and causes the time series to be non-stationary and random. At present, most of the datasets on cancer mortality in various cities and counties in Taiwan belong to non-stationary and non-seasonal time-series data. They are suitable to use in the trend extension algorithm to forecast future trends. In order to further improve the forecast accuracy, the present study improved the traditional ARIMA algorithm. First, the cancer mortality data announced by the Ministry of Health and Welfare, Taiwan, for the past 26 years from 1992 to 2017 were divided into training and test data to compare the accuracy of different improved forecast algorithms. One of the forecasting performance evaluation methods is to estimate the forecast accuracy of each algorithm via Mean Absolute Percentage Error (MAPE). Finally, the best improved ARIMA algorithm was then used to forecast the cancer mortality in Taiwan for the next five years. The forecast results will provide the relevant government agencies with prior knowledge of the possible trends of cancer mortality and act as a reference for policy planning. These would allow people (especially the elderly) to receive appropriate cancer screening mechanisms, and those who already have cancer can get proper treatment, reduce cancer mortality, and improve their quality of life.Keywords：elder health, cancer mortality, trend extension algorithm, ARIMA model