BibTeX:

@article{IJIRSTV3I6072,
     title={Literature Survey on Investigation of Chronic Disease Correlation utilizing Data Mining Techniques},
     author={Babitha M and Manikandan M},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={6},
     pages={176--179},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I6072.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

People in today’s world get affected by many diseases that do not have a complete cure. The development of one disease may lead to various other complications. A disease is an abnormal condition that affects the structure and function of one or more parts of the body. It may be caused by various factors, external and internal dysfunctions. There is a trend of various chronic diseases in any society. The major concern is that these chronic diseases are leading to many other diseases in future. An attempt to explore the correlation of various chronic diseases has become a necessity. This can be achieved by using data mining techniques, which help to derive knowledge about the affects of a particular chronic disease on the other chronic diseases. Since there is growing trend of diabetes and ischemic heart disease in the society, in this paper the focus is to investigate the effect of these diseases on the other chronic diseases using the ICD9 diagnostic codes. To achieve this goal various types of data mining techniques are used. The large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. The conclusion is an optimal set of ICD9 diagnostic codes associated with individuals having diabetes or ischemic heart disease. These codes are then examined in the light of the human anatomic systems i.e. Circulatory system, Respiratory system, Nervous system, Musculoskeletal system, Renal system and Neoplasm and their relevance is justified.

Chronic disease; ICD9 diagnostic codes; datamining; human anatomic system