Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy
Main Article Content
Abstract
Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD.
Article Details
References
Centers for Disease Control and Prevention (2017). Chronic Kidney Disease Surveillance Systems. Accesses March 9, 2017 http://www.cdc.gov/ckd
Ramakrishna, M.T.; Venkatesan, V.K.; Izonin, I.; Havryliuk, M.; Bhat, C.R. Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data. Entropy 2023, 25, 245. https://doi.org/10.3390/e25020245
Disease K. Improving global outcomes (kdigo) transplant work group. kdigo clinical practice guideline for the care of kidney transplant recipients. American Journal of Transplantation . 2009;9(3):S1–S155. doi: 10.1111/j.1600-6143.2009.02834.x.
Palma-Mendoza R. J., Rodriguez D., Marcos L. D. Distributed relieff-based feature selection in spark. Knowledge and Information Systems . 2018;57(1):1–20. doi: 10.1007/s10115-017-1145-y. [CrossRef] [Google Scholar]
Nassar M., Safa H., Mutawa A. A., Helal A., Gaba I. Chi squared feature selection over Apache spark. Proceedings of the 23rd International Database Applications & Engineering Symposium; June 2019; Athens Greece. pp. 1–5. [CrossRef] [Google Scholar]
Charleonnan A., Fufaung T., Niyomwong T., Chokchueypattanakit W., Suwannawach S., Ninchawee N. Predictive analytics for chronic kidney disease using machine learning techniques. Proceedings of the 2016 management and innovation technology international conference (MITicon); October 2016; Bang-San, Thailand.
Chittora P., Chaurasia S., Chakrabarti P., et al. Prediction of chronic kidney disease-a machine learning perspective. IEEE Access . 2021;9(17312):p. 17334. doi: 10.1109/access.2021.3053763.
T. R. M, Vinoth Kumar V, Lim S-J (2023) UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement. PLoS ONE 18(3): e0282904. https://doi.org/10.1371/journal.pone.0282904
H. Polat, H. D. Mehr, and A. Cetin, Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods, J. Med. Syst., vol. 41, no. 4, p. 55, Apr. 2017.
S. Bashir, U. Qamar, F. H. Khan, and M. Y. Javed, MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble, Arab. J. Sci. Eng., vol. 39, no. 11, pp. 77717783, Nov. 2014.
D. Devarajan D Stalin Alex, T R Mahesh, Rajanikanth Aluvalu, V Vinoth Kumar, Uma Maheswari, S Shitharth, "Cervical Cancer Diagnosis Using Intelligent Living Behaviour of Artificial Jellyfish Optimized with Artificial Neural Network," in IEEE Access, 2022, https://doi.org/10.1109/ACCESS.2022.3221451
I. Pritom, M. A. R. Munshi, S. A. Sabab, and S. Shihab, Predicting breast cancer recurrence using effective classification and feature selection technique, in 2016 19th International Conference on Computer and Information Technology (ICCIT), 2016, pp. 310314.
U. N. Dulhare and M. Ayesha, Extraction of action rules for chronic kidney disease using Naive Bayes classifier, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016, pp. 15.
Chittora P., Chaurasia S., Chakrabarti P., et al. Prediction of chronic kidney disease-a machine learning perspective. IEEE Access . 2021;9(17312):p. 17334. doi: 10.1109/access.2021.3053763
Avci E., Karakus S., Ozmen O., Avci D. Performance comparison of some classifiers on chronic kidney disease data. Proceedings of the 2018 6th International Symposium on Digital Forensic and Security (ISDFS); March 2018; Antalya, Turkey. pp. 1–4.
Abdullah A. A., Hafidz S. A., Khairunizam W. Performance comparison of machine learning algorithms for classification of chronic kidney disease (ckd) Journal of Physics: Conference Series . 2020;1529(5) doi: 10.1088/1742-6596/1529/5/052077.052077
Jena L., Patra B., Nayak S., Mishra S., Tripathy S. Intelligent and Cloud Computing . New York, NY, USA: Springer; 2021. Risk prediction of kidney disease using machine learning strategies; pp. 485–494.
Jongbo O. A., Adetunmbi A. O., Ogunrinde R. B., Ajisafe B. B. Development of an ensemble approach to chronic kidney disease diagnosis. Scientific African . 2020;8 doi: 10.1016/j.sciaf.2020.e00456.e00456
Wibawa M. S., Maysanjaya I. M. D., Putra I. M. A. W. Boosted classifier and features selection for enhancing chronic kidney disease diagnose. Proceedings of the 2017 5th international conference on cyber and IT service management (CITSM); August 2017; Denpasar, Indonesia. pp. 1–6.
Venkatesan, V.K.; Ramakrishna, M.T.; Batyuk, A.; Barna, A.; Havrysh, B. High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach. Systems 2023, 11, 81. https://doi.org/10.3390/systems11020081
“UCI Machine Learning Repository” [Online] Available:https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease.
Ifraz, G.M.; Rashid, M.H.; Tazin, T.; Bourouis, S.; Khan, M.M. Comparative Analysis for Prediction of Kidney Disease Using Intelligent Machine Learning Methods. Comput. Math. Methods Med. 2021, 2021, 6141470
S. Y. Yashfi et al., "Risk Prediction Of Chronic Kidney Disease Using Machine Learning Algorithms," 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-5, doi: 10.1109/ICCCNT49239.2020.9225548.
Venkatesan, V.K.; Ramakrishna, M.T.; Izonin, I.; Tkachenko, R.; Havryliuk, M. Efficient Data Preprocessing with Ensemble Machine Learning Technique for the Early Detection of Chronic Kidney Disease. Appl. Sci. 2023, 13, 2885. https://doi.org/10.3390/app13052885
Kovesdy, C.P.; Matsushita, K.; Sang, Y.; Brunskill, N.J.; Carrero, J.J.; Chodick, G.; Hasegawa, T.; Heerspink, H.L.; Hirayama, A.; Landman, G.W.; et al. Serum potassium and adverse outcomes across the range of kidney function: A CKD Prognosis Consortium meta-analysis. Eur. Heart J. 2018, 39, 1535–1542.
N. K. Baskaran and T. R. Mahesh, "Performance Analysis of Deep Learning based Segmentation of Retinal Lesions in Fundus Images," 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2023, pp. 1306-1313, doi: 10.1109/ICEARS56392.2023.10085616.
M. T R and G. G, "MRI techniques using image processing Brain Tumor Detection," 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), Greater Noida, India, 2023, pp. 477-480, doi: 10.1109/AISC56616.2023.10085289.
T. R. Mahesh, V. Vivek, V. V. Kumar, R. Natarajan, S. Sathya and S. Kanimozhi, "A Comparative Performance Analysis of Machine Learning Approaches for the Early Prediction of Diabetes Disease," 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 2022, pp. 1-6, doi: 10.1109/ACCAI53970.2022.9752543
P. Shrestha, A. Singh, R. Garg, I. Sarraf, T. R. Mahesh and G. Sindhu Madhuri, "Early Stage Detection of Scoliosis Using Machine Learning Algorithms," 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), 2021, pp. 1-4, doi: 10.1109/FABS52071.2021.9702699
K. K. Jha, A. K. Jha, K. Rathore and T. R. Mahesh, "Forecasting of Heart Diseases in Early Stages Using Machine Learning Approaches," 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), 2021, pp. 1-5, doi: 10.1109/FABS52071.2021.9702665.