Prediction of Diagnosing Chronic Kidney Disease using Machine Learning: Classification Algorithms
M. Prashanthi Reddy1, T. Uma Devi2
1M. Prashanthi Reddy*, PG Student, Department of CS, GIS, GITAM (Deemed to be University), Visakhapatnam, India.
2Dr.T.UmaDevi, Associate Professor, Department of CS, GIS, GITAM (Deemed to be University), Visakhapatnam, India,
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 1922-1924 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3989049620/2020©BEIESP | DOI: 10.35940/ijitee.F3989.049620
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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: Chronic Kidney Disease is a very dangerous health problem that has been spreading as well as growing due to diversification in life style such as food habits, changes in the atmosphere, etc. The branch of biosciences has progressive to a bigger extent and has bring out huge amounts of data from Electronic Health Records. The primary aim of this paper is to classify using various Classification techniques like Logistic Regression (LR), K-Nearest Neighbor (KNN) Classifier, Decision Tree Classifier Tree, Random Forest Classifier, Support Vector Machine (SVM), and SGD Classifier. According to the health statistics of India 63538 cases has been registered on chronic renal disorder. Average age of men and women susceptible to renal disorders occurs within the range of 48 to 70 years.
Keywords: Chronic kidney Disease, logistic Regression, K-Nearest Neighbor, Support vector machine (SVM).
Scope of the Article: Machine Learning