Detecting Hidden Patterns from Brucellosis Patients' Data in Khorasan Razavi Province Using Appriori Algorithm

• Main Authors: Soheil Hashtarkhani, Ali Akbar Heidari, Kobra Etminani
• Format: Article
• Language: English
• Published: Hamara Afzar 2016-07-01
• Series: Frontiers in Health Informatics
• Online Access: http://ijmi.ir/index.php/IJMI/article/view/113

Introduction: Brucellosis is a transmissible disease between humans and animals through infected animals and their products.The disease exist in most parts of the world especially in developing countries.because of the serious impact of the disease in public health and socio-economical status, controling the disease is very important in developing countries. The purpose of this article is to identify hidden patterns and relations between brucellosis patients which can be benefitial for physicians in diagnosis process. Material and Methods:  This study is a retrospective study of data collected from brucellosis Khorasan Razavi province recorded at the health center, have been used. Due to differences in format and number of features collected during different years, before processing operations carried out in several stages to the same data. Fields associated with different methods and with expert opinion was converted into discrete fields and fields lost was estimated using the EM algorithm. APPIORI algorithm analysis was performed using the hidden relationships between data found that significant relationships were infected with expert opinion. Results:  Among the 163 relationship with over 7.0 Conficence rate which Weka software was discovered, by the application in consultation with an infectious disease expert, 10 clinically significant relationship was reported. Conclusion: Diagnosig brucellosis is realy difficult to physicions because of its vagious nature and symptoms. Because many unknown relationships between risk factors and demographic characteristics of the patients, the use of data mining concepts, especially in the medical data is beneficial because usually high volume assumptions are available. further studies can test the validity of these rules like Randomize Control Trial studies.