A Comparative Study on the Pre-Processing and Mining of Pima Indian Diabetes Dataset

Amatul, Zehra and Tuty Asmawaty, Abdul Kadir and M.A. M., Aznan (2013) A Comparative Study on the Pre-Processing and Mining of Pima Indian Diabetes Dataset. In: 3rd International Conference on Software Engineering & Computer Systems (ICSECS - 2013), 20-22 August 2013 , Universiti Malaysia Pahang. pp. 1-10..

[img]
Preview
PDF (fskkp-2013-tuty-ComparativeStudyOn)
31-UMP.pdf

Download (312kB)

Abstract

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.

Item Type: Conference or Workshop Item (Speech)
Uncontrolled Keywords: Diabetes prediction; Type II Diabetes Mellitus; Data Mining; Data pre-processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Ms. Ratna Wilis Haryati Mustapa
Date Deposited: 25 Feb 2014 05:07
Last Modified: 26 Apr 2018 00:56
URI: http://umpir.ump.edu.my/id/eprint/5035
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item