Machine Learning based Exploratory Data Analysis (EDA) and Diagnosis of Chronic Kidney Disease (CKD)
DOI:
https://doi.org/10.4108/eetpht.10.5512Keywords:
Chronic Kidney Disease, Machine Learning, Classification, Feature selection, RegressionAbstract
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.
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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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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Copyright (c) 2024 Vaishali Mehta, Neera Batra, Poonam, Sonali Goyal, Amandeep Kaur, Khasim Vali Dudekula, Ganta Jacob Victor
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