Chronic Kidney Disease Prediction using Machine Learning Models
S.Revathy1, B.Bharathi2, P.Jeyanthi3, M.Ramesh4
1S,Revathy*, Information Technology, Sathyabama Institute of Science and Technology, Chennai, India.
2B.Bharathi, Information Technology, Sathyabama Institute of Science and Technology, Chennai, India.
3P.Jeyanthi, Information Technology, Sathyabama Institute of Science and Technology, Chennai, India.
4M. Ramesh, Tata Consultancy Services, Chennai, India.
Manuscript received on September 11, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6364-6367 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2213109119/2019©BEIESP | DOI: 10.35940/ijeat.A2213.109119
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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: The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage . Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing, data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD.
Keywords: Chronic Kidney Disease, Decision Tree, Machine Learning, Random Forest, Support Vactor s.