An Optimized Diagnostic Model for Chronic Kidney Disease
Abstract
Chronic Kidney Disease (CKD) is a gradual loss of renal function over a period of time. It is a major global health problem and a leading cause of death, especially in developing nations like Nigeria. It is considered a "silent killer" because there are few symptoms in its early stages. However, various researchers have employed different machine learning techniques in diagnosis of CKD. Amongst these techniques that have been employed in the diagnosis of CKD, ANFIS model outperformed other existing models. Thus, this study improved on ANFIS model by presenting An Optimized Diagnostic (AOD) model for diagnosis of CKD. AOD Model was designed using genetic algorithm for the optimization of relevant clinical decision variables of the dataset obtained from university of California Irvine machine learning repository.
A hybrid training algorithm was used to train the model. The result of this research was subjected to two main performance criteria which are convergence rate and optimum performance (sensitivity, specificity, accuracy, type I error, type II error, type I error rate and type II error rate) and was compared with an existing ANFIS model.
The AOD model converged at the 12th epoch with a minimum error of 0.7333 and a sensitivity of 100%, specificity of 89%, accuracy of 95%, type I error of 6, type II error of 0, type I error rate of 0 and type II error rate of 0.0857 while the ANFIS model converged at the 72nd epoch with minimum error of 0.7333 and a sensitivity of 100%, specificity of 77 %, accuracy of 88%, type I error of 15, type II error 0, type I error rate of 0 and type II error rate of 0.2143. This research shows that AOD model resulted in better classification accuracy and also takes lesser time to converge than ANFIS model.
Full Text:
PDFReferences
References
Abhishek, B., Gour, S. M. T., and Gupta, D. (2012). Proposing Efficient Neural Network Training Model for Kidney Stone Diagnosis. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3), 3900-3904.
. [2] AI- Hyari, A., AlTaee, A. M., and Al Taee, M. A. (2013). Clinical Decision Support for diagnosis and management of Chronic Renal Failure, Applied Electrical Engineering and Computing Technologies (AEECT), IEEE Jordan Conference on Pages 1-6.
.Akgundogdu, A., Kilic, N., Ucan, O. N., and Akalin, N. (2010). Diagnosis of renal failure disease using adaptive neuro-fuzzy inference system. Journal of Medical Systems, 34: 1003-1009.
Ashraf, M., Chetty, G., and Tran D (2013). Feature Selection Techniques on Thyroid, Hepatitis, and Breast Cancer datasets, International Journal on Data Mining and Intelligent Information Technology Applications (IJMIA), 3(1), 1-8.
Bermingham, M. L., Pong-Wong, R., Spiliopoulou, A., Hayward, C., Rudan, I., Campell, H., Wrigh, A.F., Wilson, J.F., Agakov, F., Navarro, P., and Haley, C.S. (2015). Application of high-dimensional feature selection evaluation for genomic prediction in man.scientific report: A Nature Research Journal, pp 1-12.
Bhatia, S., and Patel, S. (2015). Analysis on different Data mining Techniques and algorithms used in IOTShweta Bhatia Int. Journal of Engineering Research and Applications 5 (1-1):.82-85
Boukenze, B., Mousannif, H., and Haqiq, A. (2016). Performance of data mining techniques to predict in healthcare case study: chronic kidney failure disease. International Journal of Database Management Systems ( IJDMS ). 8 (3), 1-9. June.
Cheng-Min, C., Cheng-Tao, Y., and Bor-Wen, C. (2012). Data mining models for diagnosing acute renal failure, The International Journal of Organizational Innovation 4 (4):155-175.
Chowdhury, D. R., Chatterjee, M., and Samanta, R. K. (2011). An Artificial Neural Network Model for Neonatal Disease Diagnosis, International Journal of Artificial Intelligence and Expert Systems (IJAE), 2 (3): 96-109
Cruz, J., and Wishart, D. (2006). Applications of Machine Learning in Cancer Prediction and Prognosis, Journal of cancer informatics, 2: 59-77.
Enrique, C., Bertha, G., Oscar, F., and Amparo, A. (2006). A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis, Journal of Machine Learning Research 7, 1159–1182.
Gansevoort, R.T., Correa-Rotter, R., Hemmelgarn, BR., Jafar, TH., Heerspink H.J., Mann, J. F., Matsushita, K., and Wen, C.P. (2013). Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet 382: 339-352.
Greger, R., Schlatter, E., and Hebert, S.(2001). Milestones in nephrology: Presence of luminal K1, a prerequisite for active NaCl transport in the cortical thick ascending limb of Henle’s loop of rabbit kidney. Journal of America Society Nephrology, 12: 1788–1793.
Grzyma?a-Busse, J., and Hu, M. (2005). A Comparison of Several Approaches to Missing Attribute Values in Data Mining. In: W. Ziarko, Y. Yao, (eds.), Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, vol., pp. 378–385. Springer Berlin / Heidelberg, 2001.ISBN 978-3-540-43074-2.
Guyon, I., and Elisseeff, A. (2003). An Introduction to Variable and Feature Selection Journal of Machine Learning Research 3:1157-1182.
Hemanth, D.J., Vijila, C.K., and Anitha J. ( 2010). Application of Neuro-Fuzzy Model for MR Brain Tumor Image Classification, Biomedical Soft Computing and Human Sciences, 16 (1): 95-102
Jebari K., and Madiafi, M. (2013). Selection Methods for Genetic Algorithms.International Journal Emerg Science. IJES, 3(4), 333-344.
Jha, V., Wang, A.Y., Wang, H. (2012). The impact of CKD identification in large countries: the burden of illness. Nephrology Dialysis Transplantation 27: 32-38
Lakany, H., and Conway, B. (2007). Understanding intention of movement from electroencephalograms. Expert Systems, 24(5): 295-304.
Lakshmi. K.R., Nagesh, Y., and VeeraKrishna, M. (2014). Performance Comparison Of Three Data Mining Techniques For Predicting Kidney Dialysis Survivability, International Journal of Advances in Engineering & Technology, Mar., 7(1): 242-254
Luo, C., Zeng, J., Yuan, M., Dai, W., and Yang, Q. (2016).Telco User Activity Level Prediction with Massive Mobile Broadband Data. ACM Transactions on Intelligent Systems and Technology .
Ng, A. Y., and Jordan, M. I. (2001). Convergence rates of the voting gibbs classifier, with application to bayesian feature selection, in 18th International Conference on Machine Learning. Morgan Kaufmann.
Norouzi, J., Yadollahpour, A., Mirbagheri, S.A., Mazdeh, M., Hosseini, S.A. (2016). Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System” Computational and Mathematical Methods in Medicine, Article ID 6080814, 9 pages
Ramya, S., and Radha, N. (2016). Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithms, International Journal of Innovative Research in Computer and Communication Engineering, 4(1): 812-820.
Reddenna, L.1., Shaik, S., and Kumar K. (2014). Dialysis Treatment: A Comprehensive Description International Journal of Pharmaceutical Research & Allied Sciences 3(1): 1-13.
Rockville, P., and Suite R. (2009). American Kidney Fund 11921 Perspectives on egfr reporting from the interface between primary and secondary Clinical Journal of America Society of Nephrology; 4(2):258-260.
Rubini, J., and Eswaran, P. (2015). Generating Comparative Analysis of Early Stage Prediction of Chronic Kidney Disease. International Journal of Modern Engineering Research, 5(7): 49-55.
Sun, J., and Reddy, C. (2013).Big data analytics for healthcare.Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining .
Talavera, L, (1999). Feature selection as a preprocessing step for hierarchical clustering. In Proceedings of the 16th International Conference on Machine Learning, ICML-99: 389-397, Bled, Slovenia. I. Bratko and S. Dzeroski, eds. Morgan Kaufmann.
Vijayarani, S., and Dhayanand, S. (2015). Kidney disease prediction using SVM and ANN algorithms International Journal of Computing and Business Research (IJCBR),Volume 6 Issue 2.
Vijayarani, S., and Dhayanand, S. (2015). Data Mining Classification Algorithms For Kidney Disease Prediction”International Journal on Cybernetics & Informatics (IJCI) 4(4): 13-25.
Wagstaff, K. L., and Laidler, V. G. (2005). Making the Most of Missing Values: Object Clustering with Partial Data in Astronomy. Proceedings of Astronomical Data Analysis Software and Systems XIV, 347: 172–176. Pasadena, California, USA.
Zhu, X., Li, X., Zhang, S., Ju, C., and Wu, X. (2017).Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection. IEEE Transactions on Neural Networks and Learning Systems .
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Journal of Electrical Engineering, Electronics, Control and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.