利用症狀加權分群化技術發掘病患罹患疾病

Using Clustering Techniques with Weighted Symptoms to Find Diagnosed Diseases for Patients

本研究以病患疾病之診斷資料為探勘的資料來源，每一筆診斷資料包含病患症狀與罹患疾病，文中以某一位病患症狀為探勘目標，考量症狀加權情況下，利用資料探勘(data mining)中的分群化(clustering)方法發掘病患症狀罹患疾病傾向。文中分別以此病患症狀形成之子集合為一群組中心點，設計一個症狀加權分群化方法，將滿足最小症狀相似度的診斷資料歸屬於群組中，然後從群組中找出累計出現數量最大的疾病項目，藉此發掘病患症狀罹患疾病傾向。文中根據提出的方法，設計與建置一個病患罹患疾病診斷系統，本系統探勘結果，對一般民眾自我檢視症狀罹患疾病傾向，或是臨床經驗不足醫療人員的疾病診斷，可以提供非常有用的參考資訊。

This paper uses diagnostic data as the source of mining, and a diagnostic data contains a patient's symptoms and diseases. Clustering techniques in data mining are used to analyze tendentiousness of a patient's diagnosed diseases. Let a patient's symptoms as the target of mining and assign each subset of the symptoms as a center of a group. By considering weighted symptoms, we present a clustering method to cluster diagnostic data to groups with the center if their symptoms similarity conform the minimum similarity of symptoms. The most possible diagnosed diseases of the patient's symptoms are found from the group. According to the presented method, a mining system of diagnoses diseases for patients is designed and built. The results of mining can provide very useful information for self-diagnose diseases of people and diagnose diseases of inexperience hospital staffs.