An Effect of Machine Learning Based Classification Algorithms on Chronic Kidney Disease
Jerlin Rubini Lambert1, Pramila Arulanthu2, Eswaran Perumal3
1Jerlin Rubini Lambert*, is Currently Pursuing Ph.D in Department of Computer Applications in Alagappa University, Karaikudi, Tamil Nadu, India.
2Pramila Arulanthu is Currently Pursuing Ph.D in Department of Computer Applications in Alagappa University, Karaikudi, Tamil Nadu, India.
3Eswaran Perumal, Currently Assistant Professor in Department of Computer Applications in Alagappa University, Karaikudi, Tamil Nadu, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 2249-2256 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9012019320/2020©BEIESP | DOI: 10.35940/ijitee.C9012.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the recent days, the prediction models of chronic kidney disease (CKD) becomes significant in the area of decision making which is helpful in healthcare systems. Because of large amount of medical data, efficient models are required to obtain precise results and data classification algorithms can be employed to detect the presence of CKD. Recently, various machine learning (ML) dependent on data classifier technique is presented for forecasting CKD. Since numerous classification algorithms for CKD prediction exist, there is a need to investigate the prediction performance of these algorithms. This paper propose a comparative analysis of 4 data classifier technique such as deep learning (DL), decision tree (DT), random forest (RF) and random tree (RT). The process of classification technique is analyzed with the help of reputed CKD dataset attained from UCI repository. From the simulation outcomes, it is evident that the DL method achieved optimal classifier action with respect to various namely accuracy, precision and recall.
Keywords: Chronic Kidney Disease, Data Classification, Deep Learning, Machine learning.
Scope of the Article: Machine learning