隨著醫療科技發展、教育和經濟變動等因素,導致全球逐漸邁入高齡化社會,重視老年相關疾病議題已成為社會關注的焦點,然而近年來巴金森氏症已被列為三大老年人疾病之一,其罹患人口增加的最主要關鍵在於老年人逐年增加,故巴金森氏症為高齡化社會不可忽視的問題。而目前巴金森氏症之主要治療方法還是以內科為主、外科為輔方式進行治療,且尚無藥物可以完全治癒此疾病。本研究利用Affymetrix GeneChip Human Genome U133 Plus 2.0 Array此基因晶片的Microarray資料進行研究,分別為GSE6613和GSE8397兩組資料集,本研究先利用判別分析與二元羅吉斯迴歸篩選出可用於辦別出罹患巴金森氏症之基因,並使用支援向量機與隨機森林進行模型之建立,最後利用GO基因功能分類資料庫,其中GSE6613為血液樣本,故期盼給予未來生物與醫學之研究上,亦可以藉由血液樣本來辨別出是否為罹患巴金森氏症之高風險群。
With the development of medical technology, education and economic, the global gradually become an aging society. Age-related diseases have become the focus of attention. In recent years, Parkinson's disease has been listed as one of the three major age-related diseases. The number of patients increasing mainly due to the annual increment of the elder people. Therefore, Parkinson's disease cannot be ignored in the society. So far, the main treatment of Parkinson's disease is the medical and supplemented by the surgical. However there is no drug that can completely cure the disease. We used the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array, GSE6613 and GSE8397 data sets downloaded from NCBI. We selected the genes significantly differential expression for discriminant analysis and binary logistic regression. Then, we build the model with support vector machine and random forest. Finally, we found out the gene ontology terms the significant genes enriched, where GSE6613 is blood samples provided some useful index for researchers in distinguish Parkinson's disease in the future.