為了增加人們對疾病的瞭解，疾病間的關聯一直是熱門的研究議題。在過往的研究裡，大多研究是從基因與疾病的關係為切入的角度，以此探討疾病間的關聯程度。然而，疾病的發生往往受到病患的性別、年齡以及季節因素等等的影響，本論文利用性別及年齡將病患分成不同族群。接著，利用病患資料來探討某一疾病在特定族群中的關聯疾病。舉例來說：對於中年男性的族群當中，患有某一疾病的病人在固定時間內得到另一疾病的人數比例。我們利用資料探勘中的關聯規則演算法找出疾病關聯性，並利用疾病的盛行率以及疾病在性別、年齡以及時間上的病患分佈來過濾找到的疾病關聯，以此提升精確度。藉由專業人士評估實驗結果，顯示我們的過濾方法有不錯的表現。以應用面來說，本研究的結果將可實現許多層面的應用，像是民眾的衛生教育以及專業人士的研究材料等等。

In order to increase our understanding of diseases, the relationship among diseases becomes popular research nowadays. Several previous works focused on finding the relationships between diseases with genomes. However, relationships between diseases are also affected by many factors such as gender, age, and even seasons. In this work, we divide patients into several groups based on their gender and age. After that, we find the relationships between diseases in the groups from patient records. For example, in a group of middle-aged men, we find the percentage of patients who got a disease after a specified disease in a time period. Association rule mining is adopted to find the relationships. In order to improve the precision of the results, we utilize the prevalence of diseases and distributions of diseases on gender, age, and time to filter the association rules. The experiment results which are evaluated by professionals show that our filtering method is effective. The relationship between diseases we found can be applied to many fields, such as health education for people and research materials for researchers.

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