Performance Evaluation of Various Machine Learning Techniques Applied on UCI Data set
Nita Pankaj Shende1, G.V.S.Rajkumar2

1Nita Prakash Shende*, Ph.D, Pursuing, Department of CS & Systems Engineering, GITAM University, Andhra Pradesh, India.
2Dr. G.V.S. Raj Kumar, Ph.D, Department of CS & Systems Engineering, Andhra University, Andhra Pradesh, India.
Manuscript received on October 13, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 1897-1900 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4271119119/2019©BEIESP | DOI: 10.35940/ijitee.A4271.119119
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Abstract: Data mining techniques are used in vast fields one of them is healthcare analysis. The present research is aimed to do the experimental analysis of multiple data mining classification /prediction techniques using three different machine learning classification and prediction tools over the online healthcare datasets. In this research, we have analyze different data mining classification and prediction techniques have been tested on four different online healthcare datasets. The standards used are a percentage of accuracy and error rate of every applied classifier technique. The experimental analysis are performed using the 10 fold cross-validation technique. Best suitable classification technique for a particular online dataset is selected based on the highest classification accuracy and the least error rate as performance measurement indicators.
Keywords: Healthcare, Data Mining 10 fold Cross-validation, Classification Techniques
Scope of the Article: Healthcare Informatics