PREDICTION OF CHRONIC KIDNEY DISEASE USING DEEP NEURAL NETWORK
Abstract
eural Network (DNN) is now applied in disease prediction to detect various ailments such as heart disease and diabetes. Another disease that is causing a threat to our health is kidney disease. This disease is becoming prevalent due to substances and elements we intake. Ignoring the kidney malfunction can cause chronic kidney disease leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are mild and gradual, often go unnoticed for years only to be realized of late. We conducted our research on CKD in Bade, a Local Government Area of Yobe State in Nigeria. The area has been a center of attention by medical practitioners due to the prevalence of CKD. Unfortunately, a technical approach in culminating the disease is yet to be attained. We obtained a record of 1200 patients with 10 attributes as our dataset from Bade General Hospital and used the DNN model to predict CKD's absence or presence in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to rank the features used in predicting the CKD. The outcome revealed that two attributes: Creatinine and Bicarbonate, have the highest influence on the CKD prediction.
References
Ahmed, R. M., &Alshebly, O. Q. (2019). Prediction and Factors Affecting of Chronic Kidney Disease Diagnosis using Artificial Neural Networks Model and Logistic Regression Model. Iraqi Journal of Statistical Sciences, 16(28), 140-159, 2019[Photography].
Arbain, A. N., & Balakrishnan, B. Y. P. (2019). A Comparison of Data Mining Algorithms for Liver Disease Prediction on Imbalanced Data. International Journal of Data Science and Advanced Analytics, 1(1), 1-11.
Arafat, F., Fatema, K., & Islam, S. (2018). Classification of chronic kidney disease (ckd) using data mining techniques (Doctoral dissertation, Daffodil International University).
BaÅŸar, M. D., & Akan, A. (2018). Chronic Kidney Disease Prediction with Reduced Individual Classifiers. Electrica, 18(2), 249-255.
Chahal, A., & Gulia, P. (2019). Machine Learning and Deep Learning. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(2), 2278-3075.
Chimwayi, K. B., Haris, N., Caytiles, R. D., & Iyengar, N. C. S. (2017). Risk Level Prediction of Chronic Kidney Disease Using Neuro-Fuzzy and Hierarchical Clustering Algorithm (s).
Deshpande, A (2016, July 20).A Beginner's Guide to Understanding Convolutional Neural Networks.https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ [Photography]
Geri, G., Stengel, B., Jacquelinet, C., Aegerter, P., Massy, Z. A., &Vieillard-Baron, A. (2018). Prediction of chronic kidney disease after acute kidney injury in ICU patients: study protocol for the PREDICT multicenter prospective observational study. Annals of intensive care, 8(1), 77.
Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management, 19(2), 65.
Kumar, M. (2016). Prediction of chronic kidney disease using random forest machine learning algorithm. International Journal of Computer Science and Mobile Computing, 5(2), 24-33.
Kumar, S. (2018). Chronic Kidney Disease Prediction Using Machine Learning. International Journal of Computer Science and Information Security (IJCSIS), 16(4).
Kriplani, H., Patel, B., & Roy, S. (2019). Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique. In Computer Aided Intervention and Diagnostics in Clinical and Medical Images (pp. 179-187). Springer, Cham. Chicago.
Jain, D., & Singh, V. (2018). Features Selection and classification systems for chronic disease prediction: A review. Egyptian informatics journal, 19(3), 179-189.
Lote, C. J., &Lote, C. J. (1994). Principles of renal physiology (No. QP211 L88 1994). London: Chapman & Hall.
National Kidney Foundation (NKF). The facts about Chronic Kidney Disease (CKD). Available from: https:// www.kidney.org/kidneydisease/aboutckd.
Norouzi, J., Yadollahpour, A., Mirbagheri, S. A., Mazdeh, M. 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, 2016.
Padmanaban, K. A., & Parthiban, G. (2016). Applying machine learning techniques for predicting the risk of chronic kidney disease. Indian Journal of Science and Technology, 9(29), 1-6.
Sathya, R., & Abraham, A. (2013). Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification. International Journal of Advanced Research in Artificial Intelligence, 2(2), 34–38. https://doi.org/10.14569/IJARAI.2013.020206.
Scholar, P. G. (2018). Chronic Kidney Disease Prediction Using Machine Learning. International Journal of Computer Science and Information Security (IJCSIS), 16(4).
Ulasi, I. I., &Ijoma, C. K. (2010). The enormity of chronic kidney disease in Nigeria: the situation in a teaching hospital in South-East Nigeria. Journal of tropical medicine.
Purusothaman, G., &Krishnakumari, P. (2015). A survey of data mining techniques on risk prediction: Heart disease. Indian Journal of Science and Technology, 8(12), 1.
World Health Organization. (2006). Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation.
World Kidney Day. (2020). Chronic kidney Disease.https://www.worldkidneyday.org/facts/chronic-kidney-disease/.
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