ABSTRACT
Knowledge discovery and data mining has been increasingly popular and essential in the health care sector. Extensive applications have been reported for treatment, evaluation, drug delivery, disease predictions, anomaly detections, and customer relationship management. The use of electronic health records (EHRs) has provided access to enormous clinical data, and application of data mining techniques have helped to transform this data information into valuable knowledge for health care decision-making. The human immune system, if gone awry, can produce serious detrimental consequences. Autoimmune disorders are one form of such complications where the body's immune system tends to target the existing self antigens, leading to destruction and complications in the human body. Various data mining algorithms have been used on medical health record data sets to analyze the factors contributing significantly towards epidemiological studies. The chapter presents an overview of the methods involved in the correlation and pattern analysis of patient data pertaining to such diseases, and provides an insight on the data mining process used in the diagnosis and prediction of autoimmune diseases.
