Development of Data Mining Classification Tool Benchmarked With Weka Classifier (A Case Study of a High Blood Pressure Disease Dataset)

ALAWODE, John (2017) Development of Data Mining Classification Tool Benchmarked With Weka Classifier (A Case Study of a High Blood Pressure Disease Dataset). Advances in Multidisciplinary and Scientific Research, 3 (3). pp. 73-84.

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Abstract

The availability of enormous medical data that are not utilize for knowledge discovery is a great concern for researchers. Data mining as a field that extracts interesting patterns for knowledge discovery which enhances better decision making. The major data mining tool used for classification is WEKA, which has demonstrated high level of efficiency and effectiveness. It is therefore pertinent to have another classification model that will contest accuracy and sensitivity of WEKA classifier, and High Blood Pressure Disease dataset are used as case study. This research extracted knowledge from abundance of High Blood Pressure disease patients’ record. The acquired dataset was classified on both WEKA classifier and on the developed Model from Artificial Neural Network Algorithm, which was trained with learning vector quantization algorithm. The comparison was done based on their accuracy and It was discovered that WEKAs’ accuracy was 0.64% less than that of the developed Model (DMCT), but time taken to build WEKA model was extremely faster, for the same number of instances WEKA spent 0.66 seconds while DMCT spent 4.66 seconds.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mr. Bolanle Yisau I.
Date Deposited:	29 May 2021 15:07
Last Modified:	29 May 2021 15:07
URI:	http://eprints.federalpolyilaro.edu.ng/id/eprint/1487

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