Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD
K. B. Anusha1, T. Pandu Ranga Vital2, K. Sangeeta3
1K. B. Anusha, Assistant Professor, Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh , India.
2Dr. T. PanduRanga Vital (Corresponding Author), Associate Professor, Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India.
3K. Sangeeta, Sr. Assistant Professor, Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India.
Manuscript received on 12 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 2550-2563 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1792078219/19©BEIESP | DOI: 10.35940/ijrte.B1792.078219
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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 (CKD) is one of the most widely spread diseases across the world. Mysteriously some of the areas in the world like Srilanka, Nicrgua and Uddanam (India), this disease affect more and it is cause of thousands of deaths particular areas. Now days, the prevention with utilizing statistical analysis and early detection of CKD with utilizing Machine Learning (ML) and Neural Networks (NNs) are the most important topics. In this research work, we collected the data form Uddanam (costal area of srikakulam district, A.P, India) about patient’s clinical data, living styles (Habits and culture) and environmental conditions (water, land and etc.) data from 2016 to 2019. In this paper, we conduct the statistical analysis, Machine Learning (ML) and Neural Network application on clinical data set of Uddanam CKD for prevention and early detection of CKD. As per statistical analysis we can prevent the CKD in the Uddanam area. As per ML analysis Naive Bayes model is the best where the process model is constructed within 0.06 seconds and prediction accuracy is 99.9%. In the analysis of NNs, the 9 neurons hidden layer (HL) Artificial Neural Network (ANN) is very accurate than other all models where it performs 100% of accuracy for predicting CKD and it takes the 0.02 seconds process time.
Index Terms: Artificial Neural Network (ANN), Chronic Kidney Disease (CKD), Machine Learning (ML), Statistical Analysis
Scope of the Article: Machine Learning