A Systematic Literature Review on Detection and Prevention of Diabetes Using Data Warehousing and Data Mining Techniques

Abstract

One of the most significant health issue faced by all the human being these days is diabetes. Diabetes is one of the dangerous diseases to cause health care crisis worldwide and also one of the leading causes of mortality and morbidity. The common sites of Diabetes have varied distribution in different geographical locations. The main objective of the present article is to conduct a systematic literature review on detection and prevention of Diabetes of various types i.e., Type – I, Type – II and Gestational Diabetes using several types of data mining and warehousing techniques. This would help the researchers to know various data mining algorithm and method for the prediction of diabetes mellitus. We have analyzed various publications and journals and selected 25 articles which represent various data mining and warehousing methods used for diabetes research for predicting diabetes. The various techniques used are OLAP operations, Decision Tree, Naive Bayes, k-NN, k-means algorithm, classification and clustering. The data mining and warehousing techniques applied in the selected articles were useful for retrieving useful information and framing new hypothesis for further experimentation and improving the health care for diabetic patients by predicting various diseases and find out the efficient ways to treat them in advance.