A Comparative Study on the Pre-Processing and Mining of Pima Indian Diabetes Dataset
Data mining in medical data has successfully converted raw data into useful information. This information helps the medical experts in improving the diagnosis and treatment of diseases. In this paper, we review studied data mining applications applied exclusively on an open source diabetes dataset....
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5035/ http://umpir.ump.edu.my/id/eprint/5035/1/31-UMP.pdf |
| Summary: | Data mining in medical data has successfully converted raw data into useful information. This information helps the medical experts in improving the diagnosis and treatment of diseases. In this paper, we review studied data mining applications applied exclusively on an open source diabetes dataset. Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 346 million people are suffering from diabetes worldwide. Diagnosis or prediction of diabetes is done through various data mining techniques such as association, classification, clustering and pattern recognition. The study led to the related open issues of identifying the need of a relation between the major factors that lead to the development of diabetes. This is possible by mining patterns found between the independent and dependant variables in the dataset. This paper compares the classification accuracies of non-processed and pre-processed data. The results clearly show that the pre-processed data gives better classification accuracy. |
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