隨著老年人口的增加，一些老年較容易罹患的疾病也逐年上升，而當中失智症是當中影響較大的疾病之一，失智症除了會造成患者身心上的問題之外，患者的看護也是一個嚴重的問題。而近幾年也有文獻指出空氣汙染會影響失智症，有鑑於此，本研究希望能從政府所提供的資料去進行分析，找出失智症相關疾病與空氣汙染之間的關聯性。本研究透過台灣的健保資料庫所蒐集的資料進行失智症及其共伴疾病（會互相影響的病症）與政府的開放資料庫當中的空氣汙染資訊做結合去進行多目標分析。首先針對這些資料進行清理與組合，接著透過非凌駕式排序基因演算法連續分群分類資料分析架構（NSGAII-SCC），此方法主要是透過基因演算法的方式進行分群分類找出多目標因子之間可能的關聯。研究結果顯示失智症在分群結果上以抑鬱症最容易取得良好的分群結果，並且在一氧化氮（NO）、臭氧（O3）、非甲烷碳氫化合物（NMHC）、甲烷（CH4）的項目上有機會能夠取得不差的準確率。而選取資料進入詳細分析後可以看見此筆資料的分群方式與分類結果，此演算法將現實中可能產生關連的資料透過機器學習的方式不斷進行迭代與測試，最後找出可能的最佳解。此舉節省了許多資料分析上的人力時間並達成了人工無法進行的多次因子測試，來找出隱藏在資料內的可能關聯。

With the growing number of the elderly population, some diseases that are more likely to suffer the elderly are also increasing year by year. Among them, dementia is one of the most influential diseases. In addition to the mental and physical problems of the dementia, the patient's care is also a serious problem. In recent years, there have been literature stated that air pollution affects dementia. Thus, this study would like to analyze the information provided by the government to find out the orrelation between dementia-related diseases and air pollution. This study combines information on dementia and its comorbidity (which affect each other) with the air pollution information in the government's open database through the data collected by Taiwan's health insurance database for multi-objective analysis. Firstly, this research focus on data cleaning and combining, and then through the non-overriding sorting algorithm, the continuous clustering classification data analysis framework (NSGAII-SCC), which is mainly used to classify and identify multi-target factors through genetic algorithm to find the possible association. The results of the study show that dementia is most likely to achieve good clustering results in Depression, and there is an opportunity to achieve good results in NO, O3, NMHC and CH4 to achieve well accuracy. After selecting the data into the detailed analysis, we can see thes algorithm will continuously iterate and test the related data in reality through machine learning, and finally find the best possible solution. This method saves a lot of human time in data analysis and achieves multiple factor tests that impossibly handled manually, in order to find possible associations hidden within the data.

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