Paper Title

Abstract - Costofillness (COI) studies measure the economic burden of a disease or diseases and estimatethe maximum amount that could potentially be saved or gained if a disease were to be eradicated.On the other hand, COI studies are a decision instrument in public health policy. Knowledge of COI can help policy makers to decide which diseases need to be addressed first by health care and prevention policy. Additionally, these studies can indicate for which diseases cures would be valuable in reducing the burden of disease. The aim of COI studies is to itemize, value, and sum the costs of a particular problem with the aim of giving an idea of its economic burden. This requires recognizing, identifying, listing, measuring and valuing the costs generated by an illness. The appropriate data source to use for a COI study varies by the illness,perspective, and approach of the study. Governments have begun to use big data by leveraging the power of computers. But automation alone is not enough for effective data-management. The main problem is to identify the illness that have a economic burden to government and reach the direct and indirect cost data of selected illness, then determine the cost items of selected illnessinbig data. The need to analyze and extract useful knowledge from this data lead to a new generation of tools and techniques grouped under the term of data mining.Koyuncugil and Ozgulbas (2010) described the data mining as ‘‘collection of evolved statistical analysis, machine learning and pattern recognition methods via intelligent algorithms which are using for automated uncovering and extraction process of hidden predictional information, patterns, relations, similarities or dissimilarities in (huge) data’’.The aim of this study is to present a data mining algorithm for COI studies. Model contains; identifying illness in big data; determining undiscovered patterns of illness, determining direct and indirect cost that of illness, analyzing COI and clarify the variables thateffect the COI. Keywords - Big Data, Data Mining, Cost Analysis, Economic Burden of Diseases, Cost of Illness