Türkiye’de Kronik Hastalarda Multimorbiditenin Birliktelik Kuralları ile İncelenmesi

Abstract

Chronic diseases and multimorbidity are the main health and development problems facing humanity in our country as well as all over the world. According to the definition of the World Health Organization (WHO), multimorbidity is the simultaneous presence of two or more health problems in an individual. With the advancement of computer science and technology, the personal data of patients in the field of health are being recorded much more and these data are constantly increasing. Data mining methods make valuable, hidden information available from this enormous amount of data. The use of these methods in the field of health is rapidly becoming widespread as it develops a different perspective that supports strategic decisions for decision makers. In this study, association rule analysis, one of the data mining methods, was used to identify patterns of chronic diseases and risk factors by age and gender. In the study, the micro dataset of the 2022 Turkey Health Survey (THS) conducted by the Turkish Statistical Institute (TurkStat) was used as the application data. Association rule analysis allowed easy interpretation of multimorbidities with measures such as support, reliability and relevance coefficient obtained from the rules it produced. More association rules were obtained for individuals of female gender and individuals aged 65 and over. As a result of the study, neck and back disorders, infarction and coronary heart disease, asthma and bronchitis, kidney diseases and urinary incontinence were found to be the most common diseases for both sex and age groups.