Abstract
In the present work, a genetic algorithm (GA) trained neural network (NN)-based model has been proposed to detect chronic kidney disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences of CKD could be unaffordable to many, if not detected at an earlier stage. Motivated by this, the NN-GA model has been proposed which significantly overcomes the problem of using local search-based learning algorithms to train NNs. The input weight vector of the NN is gradually optimized by using GA to train the NN. The model has been compared with well-known classifiers like Random Forest, Multilayer Perception Feedforward Network (MLP-FFN), and also with NN. The performance of the classifiers has been measured in terms of accuracy, precision, recall, and F-Measure. The experimental results suggest that NN-GA-based model is capable of detecting CKD more efficiently than any other existing model.
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References
World Health Organization: Preventing Chronic Disease: A Vital Investment. WHO, Geneva (2005)
Keane, W.F., Zhang, Z., Lyle, P.A., et al.: Risk scores for predicting outcomes in patients with type 2 diabetes and nephropathy: the RENAAL study. Clin. J. Am. Soc. Nephrol. 1(4), 761–767 (2006)
Agarwal, S.K., Dash, S.C., Irshad, M., et al.: Prevalence of chronic renal failure in adults in Delhi, India. Nephrol. Dial. Transplant. 20, 1638–1642 (2005)
Modi, G.K., Jha, V.: The incidence of end-stage renal disease in India: a population-based study. Kidney Int. 70, 2131–2133 (2006)
Sakhuja, V., Jha, V., Ghosh, A.K., Ahmed, S., Saha, T.K.: Chronic renal failure in India. Nephrol. Dial. Transplant. 9, 871–872 (1994)
Mittal, S., Kher, V., Gulati, S., Agarwal, L.K., Arora, P.: Chronic renal failure in India. Ren. Fail. 19, 753–770 (1997)
Mani, M.K.: Chronic renal failure in India. Nephrol. Dial. Transplant. 8, 684–689 (1993)
Dash, S.C., Agarwal, S.K.: Incidence of chronic kidney disease in India. Nephrol. Dial. Transplant. 21, 232–233 (2006)
Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 31–44 (1996)
Chatterjee, S., Chakraborty, R., Hore, S.: A quality prediction method of weight lifting activity. In: An IET International Conference on Proceedings of the Michael Faraday IET International Summit—2015 (MFIIS-2015). 12–13 Sept 2015 in Kolkata, India (in press)
Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Neural network ensemble based CAD system for focal liver lesions using B-mode ultrasound. J. Digit. Imaging 27(4), 520–537 (2014). doi:10.1007/s10278-014-9685-0
Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J. Med. Eng. Technol. 37(4), 292–306 (2013)
Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Prediction of cirrhosis based on singular value decomposition of gray level co-occurence matrix and a neural network classifier. In: Proceedings of the IEEE International Conference on Development in E-Systems Engineering, DeSe-2011, Dubai, pp. 146–151 (2011) (Published on IEEE Xplore)
Chiu, R.K., Chen, R.Y., Wang, S.-A., Chang, Y.-C., Chen, L.C.: Intelligent systems developed for the early detection of chronic kidney disease. In: Advances in Artificial Neural Systems, p. 1 (2013)
Hornik, K.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Møller, Martin Fodslette: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)
Kulkarni, V.Y., Sinha, P.K.: Random forest classifiers: a survey and future research directions. Int. J. Adv. Comput. 36(1), 1144–1153 (2013)
Lichman, M.: UCI machine learning repository (http://archive.ics.uci.edu/ml). University of California, School of Information and Computer Science, Irvine, CA (2013)
Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77–89 (1997)
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Hore, S., Chatterjee, S., Shaw, R.K., Dey, N., Virmani, J. (2018). Detection of Chronic Kidney Disease: A NN-GA-Based Approach. In: Panigrahi, B., Hoda, M., Sharma, V., Goel, S. (eds) Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_13
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