Machine Learning based Exploratory Data Analysis (EDA) and Diagnosis of Chronic Kidney Disease (CKD)

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5512

Keywords:

Chronic Kidney Disease, Machine Learning, Classification, Feature selection, Regression

Abstract

INTRODUCTION: This research paper presents an exploratory data analysis (EDA) approach to diagnose Chronic Kidney Disease (CKD) using machine learning algorithms.

OBJECTIVES: This paper focuses on early and accurate detection of CKD using a comprehensive dataset of clinical and laboratory parameters to minimize the risk of patients’ health complications with timely intervention through appropriate medications.

METHODS: Machine Learning based prediction models including Naive Bayes, KNN, Logistic regression, decision tree, ensemble modelling, Random Forest and Ada Boost.

RESULTS: The results indicate that the Naive Bayes algorithm achieved highest accuracy and sensitivity in detecting CKD.

CONCLUSION: For reduced features and for binary class classification, Naive Bayes classifier gives best performance in terms of accuracy and computational cost. Other algorithms are good for multi-class classification but for binary class, they are little expensive than Naive Bayes.

Downloads

Download data is not yet available.

References

Almasoud, M., & Ward, T. E. Almasoud, M., & Ward, T. E. Detection of Chronic Kidney Disease Using Machine Learning Algorithms with Least Number of Predictors. 2014. Int. Jr. of Adv. Comp. Sci. and Applications (IJACSA), 10(8). http://dx.doi.org/10.14569/0100813 DOI: https://doi.org/10.14569/IJACSA.2019.0100813

Baidya, D., Umaima, U., Islam, M. N., Shamrat, F. M. J. M., Pramanik, A., & Rah-man, M. S. A Deep Prediction of Chronic Kidney Disease by Em-ploying Machine Learning Method. 2022. In: 6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 - Proceedings, 1305–1310. https://doi.org/10.1109/9776876 DOI: https://doi.org/10.1109/ICOEI53556.2022.9776876

Chittora, P., Chaurasia, S., Chakrabarti, P., Kumawat, G., Chakrabarti, T., Le-onowicz, Z., Jasinski, M., Jasinski, L., Gono, R., Jasinska, E., & Bolshev, V. Prediction of Chronic Kidney Disease - A Machine Learning Per-spective. 2021. IEEE Access, 9, 17312–17334. https://doi.org/10.1109/ 3053763 DOI: https://doi.org/10.1109/ACCESS.2021.3053763

Elhoseny, M., Shankar, K., & Uthayakumar, J. Intelligent Diagnostic Pre-diction and Classification System for Chronic Kidney Disease. 2019. Scientific Reports. 9(1), 1–14. https://doi.org/10.1038/s41598-019-46074-2 DOI: https://doi.org/10.1038/s41598-019-46074-2

Goyal, S., Batra, N., & Chhabra, K. Diabetes Disease Diagnosis Using Machine Learning Approach. 2023. Lecture Notes in Networks and Systems, 47(3), 229–237. https://doi.org/10.1007/978-981-19-2821-5_19 DOI: https://doi.org/10.1007/978-981-19-2821-5_19

Laaksonen, J., & Oja, E.. Classification with learning k-nearest neighbors. 1996. In: IEEE International Conference on Neural Networks - Conference Proceedings, 3, 1480–1483. https://doi.org/10.1109/ICNN.1996.549118 DOI: https://doi.org/10.1109/ICNN.1996.549118

Mangla, M., Akhare, R., Deokar, S., & Mehta, V.. Employing Machine Learning for Multi-perspective Emotional Health Analysis. 2020. Emotion and In-formation Processing, 199–211. https://doi.org/10.1007/978-3-030-48849-9_13 DOI: https://doi.org/10.1007/978-3-030-48849-9_13

Manonmani, M., & Balakrishnan, S. Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset. 2020. Procedia Computer Science, 171, 1660–1669. https://doi.org/10.1016/ 04.178 DOI: https://doi.org/10.1016/j.procs.2020.04.178

Nikhila. Chronic kidney disease prediction using machine learning ensemble algorithm. In: Proceedings of IEEE 2021 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2021, 476–480. https://doi.org/10.1109/ 9397144 DOI: https://doi.org/10.1109/ICCCIS51004.2021.9397144

Qezelbash-Chamak, J., Badamchizadeh, S., Eshghi, K., & Asadi, Y. A survey of machine learning in kidney disease diagnosis. Machine Learning with Applications. 2022. 10, 100418. https://doi.org/10.1016/j.mlwa.2022.100418 DOI: https://doi.org/10.1016/j.mlwa.2022.100418

Radovic, M., Ghalwash, M., Filipovic, N., & Obradovic, Z. Minimum re-dundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics. (2017). 18(1), 1–14. https://doi.org/10.1186/S12859-016-1423-9/FIGURES/6 DOI: https://doi.org/10.1186/s12859-016-1423-9

Ren, Y., Zhang, L., & Suganthan, P. N. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions. 2016. IEEE Computational Intelligence Magazine, 11(1), 41–53. https://doi.org/10.1109/MCI.2015.2471235 DOI: https://doi.org/10.1109/MCI.2015.2471235

Sharma, N., Dev, J., Mangla, M., Wadhwa, V. M., Mohanty, S. N., & Kakkar, D. A Heterogeneous Ensemble Forecasting Model for Disease Prediction. 2021. New Generation Computing, 39(3–4), 701–715. https://doi.org/10.1007/S00354-020-00119-7/TABLES/6 DOI: https://doi.org/10.1007/s00354-020-00119-7

Ali, Farman, Khalid, Hira, Zhaid K., Muhhamad, Mehmood, Gulzar & Shuaib Quershi, Muhammad. Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease. 2023. Computational Intelligence and Neuroscience, Hindawi. https://doi.org/10.1155/2023/9266889. DOI: https://doi.org/10.1155/2023/9266889

Downloads

Published

22-03-2024

How to Cite

1.
Mehta V, Batra N, Poonam, Goyal S, Kaur A, Dudekula KV, Victor GJ. Machine Learning based Exploratory Data Analysis (EDA) and Diagnosis of Chronic Kidney Disease (CKD). EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 22 [cited 2024 Apr. 21];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5512