A Comparative Analysis of Machine Learning and Deep Learning Approaches for Prediction of Chronic Kidney Disease Progression





Logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, Support Vector Machine, X Gradient Boosting


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.


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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. DOI: https://doi.org/10.7748/phc2008.

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




How to Cite

S. Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “A Comparative Analysis of Machine Learning and Deep Learning Approaches for Prediction of Chronic Kidney Disease Progression”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

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