Research Paper | Computer Science | India | Volume 9 Issue 12, December 2020
Prevention and Detection of Diabetes (Type-I & Type-II) using Data Warehousing and Data Mining Techniques in Andaman & Nicobar Islands
Deepa.S, Dr. B. Booba
Abstract: One of the most significant health issue faced by all the human being these days is diabetes. Diabetes is one of the leading causes of mortality and morbidity worldwide. The common sites of Diabetes have varied distribution in different geographical locations. The present study is conducted to detect and prevent Diabetes of two major types i. e. , Type – I and Type - II using data mining and warehousing techniques in Andaman and Nicobar Islands. The study uses data mining techniques such as classification, clustering and prediction to identify potential diabetes patients. For that a multidimensional architectural diabetes data warehouse will be built specifically to store and process diabetes-related database which include patient’s general and medical records and also a data mining model is proposed to be build and will be implemented within the diabetes data warehouse which can predict a person’s predisposition towards diabetes and generate the risk level for a particular type of diabetes and the exact method of clinical diagnosis. The k-means clustering algorithm is used for partitioning the data into diabetes and non-diabetes clusters, where the initial cluster center is represented by the mean value of the weightage of significant patterns.
Keywords: Andaman & Nicobar Islands, Diabetic patients, Data Mining and Warehousing Techniques, Multidimensional Star Schema, k- means Algorithm, OLAP Operations, Types of Diabetes- Type-I & Type-II, WAM
Edition: Volume 9 Issue 12, December 2020,
Pages: 65 - 70
How to Cite this Article?
Deepa.S, Dr. B. Booba, "Prevention and Detection of Diabetes (Type-I & Type-II) using Data Warehousing and Data Mining Techniques in Andaman & Nicobar Islands", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=SR201130160830, Volume 9 Issue 12, December 2020, 65 - 70
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