隨著少子化的時代來臨,又加上平均結婚年齡的上升,接受人工生殖技術(Assisted Reproductive Technology,ART)的人數逐年增加,人工受精(Artificial Insemination,AI)其成功率也成為近年來常常被討論的議題。目前國內有許多醫療機構皆有幫助病患做試管嬰兒胚胎植入術(In Vitro Fertilization,IVF),為了維持其準確性,應用人工智慧與資料探勘的技術,來解決醫學問題是個非常重要的課題。本研究利用案例式推理方法(Case-Based Reasoning,CBR)建構出一人工生殖案例的推理機制,藉由舊案例的所有評估資料,進行新個案之指標相似度(Similarity)的比對,再配合層級分析法(Analytic Hierarchy Process,AHP)算出各個指標的權重值,以有效的推算出一個最接近目前新個案狀況的舊有個案,以協助醫師於人工生殖手術時可依照舊有先例,進行診治,遇到新情況也可以累積新案例,以建立更完整的案例庫。
With the era of declining birthrate, coupled with the rising average age of marriage, to accept the number of assisted reproductive technology increases every year, the success rate of artificial insemination has become a frequently discussed topic in recent years. Currently there are many medical institutions Jie help patients IVF embryo implantation, in order to maintain its accuracy, application of artificial intelligence and data mining technology to solve medical problems is a very important issue. In this study, case-based reasoning construct an artificial reproductive case-based reasoning mechanism, the case of all assessments by the old data, indicators for new cases of similarity comparison of the re-calculated with the AHP weights of individual indicators to effectively calculate a new case closest to the current state of the old cases in order to assist physicians in the usual human reproductive surgery to follow precedent, the diagnosis and treatment, encounter new situations can also accumulate new case, to establish a more complete case-base .