A Comparative Analysis of Machine Learning and Deep Learning Approaches for Prediction of Chronic Kidney Disease Progression
DOI:
https://doi.org/10.4108/eetiot.5325Keywords:
Logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, Support Vector Machine, X Gradient BoostingAbstract
Chronic kidney disease is a significant health problem worldwide that affects millions of people, and early detection of this disease is crucial for successful treatment and improved patient outcomes. In this research paper, we conducted a comprehensive comparative analysis of several machine learning algorithms, including logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, Support Vector Machine, X Gradient Boosting, Decision Tree Classifier, Grid Search CV, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, XgBoost, Cat Boost Classifier, Extra Trees Classifier, KNN, MLP Classifier, Stochastic gradient descent, and Artificial Neural Network, for the prediction of kidney disease. In this study, a dataset of patient records was utilized, where each record consisted of twenty-five clinical features, including hypertension, blood pressure, diabetes mellitus, appetite and blood urea. The results of our analysis showed that Artificial Neural Network (ANN) outperformed other machine learning algorithms with a maximum accuracy of 100%, while Gaussian Naive Bayes had the lowest accuracy of 94.0%. This suggests that ANN can provide accurate and reliable predictions for kidney disease. The comparative analysis of these algorithms provides valuable insights into their strengths and weaknesses, which can help clinicians choose the most appropriate algorithm for their specific requirements.
Downloads
References
Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X. and Qin, X., 2021. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nature Biomedical Engineering, 5(6), pp.533-545. DOI: https://doi.org/10.1038/s41551-021-00745-6
Gudeti, Bhavya, Shashvi Mishra, Shaveta Malik, Terrance Frederick Fernandez, Amit Kumar Tyagi, and Shabnam Kumari. "A novel approach to predict chronic kidney disease using machine learning algorithms." In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1630-1635. IEEE, 2020. DOI: https://doi.org/10.1109/ICECA49313.2020.9297392
Sawhney, Rahul, et al. "A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease." Decision Analytics Journal 6 (2023): 100169. DOI: https://doi.org/10.1016/j.dajour.2023.100169
Singh, Vijendra, Vijayan K. Asari, and Rajkumar Rajasekaran. "A deep neural network for early detection and prediction of chronic kidney disease." Diagnostics 12.1 (2022): 116. DOI: https://doi.org/10.3390/diagnostics12010116
Xiao, Jing, et al. "Comparison and development of machine learning tools in the prediction of chronic kidney disease progression." Journal of translational medicine 17.1 (2019): 1-13. DOI: https://doi.org/10.1186/s12967-019-1860-0
Ma, Fuzhe, et al. "Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network." Future Generation Computer Systems 111 (2020): 17-26. DOI: https://doi.org/10.1016/j.future.2020.04.036
Chen, Guozhen, et al. "Prediction of chronic kidney disease using adaptive hybridized deep convolutional neural network on the internet of medical things platform." IEEE Access 8 (2020): 100497-100508. DOI: https://doi.org/10.1109/ACCESS.2020.2995310
Baidya, Deepanita, et al. "A deep prediction of chronic kidney disease by employing machine learning method." 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2022. DOI: https://doi.org/10.1109/ICOEI53556.2022.9776876
Ebiaredoh-Mienye, Sarah A., et al. "A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease." Bioengineering 9.8 (2022): 350. DOI: https://doi.org/10.3390/bioengineering9080350
Kamate, S., Veerappan, I., Sethuraman, R., Chandel, V., Patil, S., & Ananthasubramani, R. (2023). WCN23-0673 Predicting Salt Intake And Alerting Renal Failure From A Single Spot Urine Test In Healthy And Ckd Population: A Case Control Observational Study. Kidney International Reports, 8(3), S217–S218. https://doi.org/10.1016/j.ekir.2023.02.488 DOI: https://doi.org/10.1016/j.ekir.2023.02.488
Park, J. I., Baek, H., Kim, B. R., & Jung, H. H. (2017). Comparison of urine dipstick and albumin:creatinine ratio for chronic kidney disease screening: A population-based study. PloS One, 12(2), e0171106–e0171106. https://doi.org/10.1371/journal.pone.0171106 DOI: https://doi.org/10.1371/journal.pone.0171106
Sumida, K., Nadkarni, G. ., Grams, M. ., Sang, Y., Ballew, S. H., Coresh, J., Matsushita, K., Surapaneni, A., Brunskill, N., Chadban, S. ., Chang, A. ., Cirillo, M., Daratha, K. ., Gansevoort, R. ., Garg, A. ., Iacoviello, L., Kayama, T., Konta, T., Kovesdy, C. ., … Heerspink, H. . (2020). Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis : An Individual Participant-Based Meta-analysis. Annals of Internal Medicine, 173(6), 426–435. https://doi.org/10.7326/M20-0529 DOI: https://doi.org/10.7326/M20-0529
Drawz, P. E., Alper, A. B., Anderson, A. H., Brecklin, C. S., Charleston, J., Chen, J., Deo, R., Fischer, M. J., He, J., Hsu, C.-Y., Huan, Y., Keane, M. G., Kusek, J. W., Makos, G. K., Miller, 3rd, Edgar R, Soliman, E. Z., Steigerwalt, S. P., Taliercio, J. J., Townsend, R. R., … Rahman, M. (2016). Masked Hypertension and Elevated Nighttime Blood Pressure in CKD: Prevalence and Association with Target Organ Damage. Clinical Journal of the American Society of Nephrology, 11(4), 642–652. https://doi.org/10.2215/CJN.08530815 DOI: https://doi.org/10.2215/CJN.08530815
Murphy, D., & Drawz, P. E. (2019). Blood Pressure Variability in CKD: Treatable or Hypertension's Homocysteine? Clinical Journal of the American Society of Nephrology, 14(2), 175–177. https://doi.org/10.2215/CJN.14991218 DOI: https://doi.org/10.2215/CJN.14991218
Nyvad, J., Christensen, K. L., Andersen, G., Reinhard, M., Nielsen, S., Thomsen, M., Jensen, J. M., N⊘rgaard, B. L., & Buus, N. H. (2022). AORTIC CALCIFICATION INCREASES CENTRAL BLOOD PRESSURE RELATIVE TO BRACHIAL BLOOD PRESSURE IN CKD PATIENTS – A STUDY IN PATIENTS UNDERGOING ELECTIVE CORONARY ANGIOGRAPHY. Journal of Hypertension, 40(Suppl 1), e43. https://doi.org/10.1097/01.hjh.0000835628.09394.f9 DOI: https://doi.org/10.1097/01.hjh.0000835628.09394.f9
Dritsas, Elias, and Maria Trigka. "Machine learning techniques for chronic kidney disease risk prediction." Big Data and Cognitive Computing 6.3 (2022): 98. DOI: https://doi.org/10.3390/bdcc6030098
Mondol, Chaity, et al. "Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models." Algorithms 15.9 (2022): 308. DOI: https://doi.org/10.3390/a15090308
Jha, Vivekanand, Prof, Garcia-Garcia, Guillermo, Prof, Iseki, Kunitoshi, Prof, Li, Zuo, MD, Naicker, Saraladevi, Prof, Plattner, Brett, MD, Saran, Rajiv, Prof, Wang, Angela Yee-Moon, Prof, & Yang, Chih-Wei, Prof. (2013). Chronic kidney disease: global dimension and perspectives. The Lancet (British Edition), 382(9888), 260–272. https://doi.org/10.1016/S0140-6736(13)60687-X DOI: https://doi.org/10.1016/S0140-6736(13)60687-X
Remuzzi, G., & Bertani, T. (1998). Pathophysiology of Progressive Nephropathies. The New England Journal of Medicine, 339(20), 1448–1456. https://doi.org/10.1056/NEJM199811123392007 DOI: https://doi.org/10.1056/NEJM199811123392007
Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E., & Hsu, C. Y. (2004). Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. ACC Current Journal Review, 13(12), 13–13. https://doi.org/10.1016/j.accreview.2004.11.016 DOI: https://doi.org/10.1016/j.accreview.2004.11.016
Luyckx, V. A., Tonelli, M., & Stanifer, J. W. (2018). The global burden of kidney disease and the sustainable development goals. Bulletin of the World Health Organization, 96(6), 414–422D. https://doi.org/10.2471/BLT.17.206441 DOI: https://doi.org/10.2471/BLT.17.206441
Crowe, E., Forrest, C., Mclntyre, N., & O'Riordan, S. (2008). Early identification and management of chronic kidney disease in primary care. Primary Health Care, 18(10), 29–33. https://doi.org/10.7748/phc2008.12.18.10.29.c6888 DOI: https://doi.org/10.7748/phc2008.12.18.10.29.c6888
Hall, J. E., Brands, M. W., & Henegar, J. R. (1999). Mechanisms of Hypertension and Kidney Disease in Obesity. Annals of the New York Academy of Sciences, 892(1), 91–107. https://doi.org/10.1111/j.1749-6632.1999.tb07788.x DOI: https://doi.org/10.1111/j.1749-6632.1999.tb07788.x
ZEISBERG, M., & NEILSON, E. G. (2010). Mechanisms of Tubulointerstitial Fibrosis. Journal of the American Society of Nephrology, 21(11), 1819–1834. https://doi.org/10.1681/ASN.2010080793 DOI: https://doi.org/10.1681/ASN.2010080793
Shanmuganathan, R., Ramanathan, K., Padmanabhan, G., & Vijayaraghavan, B. (2017). Evaluation of Interleukin 8 gene polymorphism for predicting inflammation in Indian chronic kidney disease and peritoneal dialysis patients. Alexandria Journal of Medicine, 53(3), 215–220. https://doi.org/10.1016/j.ajme.2016.06.004 DOI: https://doi.org/10.1016/j.ajme.2016.06.004
Ketteler, M., Block, G. A., Evenepoel, P., Fukagawa, M., Herzog, C. A., McCann, L., Moe, S. M., Shroff, R., Tonelli, M. A., Toussaint, N. D., Vervloet, M. G., & Leonard, M. B. (2017). Executive summary of the 2017 KDIGO Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Guideline Update: what’s changed and why it matters. Kidney International, 92(1), 26–36. https://doi.org/10.1016/j.kint.2017.04.006 DOI: https://doi.org/10.1016/j.kint.2017.04.006
Delanaye, P., Jager, K. J., Bökenkamp, A., Christensson, A., Dubourg, L., Eriksen, B. O., Gaillard, F., Gambaro, G., van der Giet, M., Glassock, R. J., Inidason, O. S., van Londen, M., Mariat, C., Melsom, T., Moranne, O., Nordin, G., Palsson, R., Pottel, H., Rule, A. D., … van den Brand, J. A. J. G. (2019). CKD: A Call for an Age-Adapted Definition. Journal of the American Society of Nephrology, 30(10), 1785–1805. https://doi.org/10.1681/ASN.2019030238. DOI: https://doi.org/10.1681/ASN.2019030238
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6
Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9. https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016
Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937
Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052
Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603
Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579
Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484
Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470. DOI: https://doi.org/10.3390/w13233470
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
