Data Analysis on Chronic Kidney Disease Prognosis
Divya Jain1, Parshavi Bolya2, Aaditya Maheshwari3, Yogendra Singh Solanki4
1Divya Jain, Department of Computer Science & Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
2Parshavi Bolya, Department of Computer Science & Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
3Aaditya Maheshwari, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
4Yogendra Singh Solanki, Assistant Professor, Department of Electronics and Communications, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
Manuscript received on 24 February 2020 | Revised Manuscript received on 10 March 2020 | Manuscript Published on 18 March 2020 | PP: 130-132 | Volume-8 Issue-6S March 2020 | Retrieval Number: F10230386S20/2020©BEIESP | DOI: 10.35940/ijrte.F1023.0386S20
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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: We have taken our dataset from UCI Machine Learning Repository. Our study is about Chronic Kidney Diseases based on 24 input attributes to produce one output attribute i.e. a patient is suffering from chronic kidney disease or not. We have used three major attributes in our study i.e. PCV, RBCC and Hemoglobin with respect to Age for optimum result. These attributes play major role in our study.
Keywords: CKD, Analytics, Weka, PCV, RBCC, Hemoglobin.
Scope of the Article: Data Analytics