Research Article
Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease
Table 3
Comparison of classifiers for CKD.
| S. no | Authors | Year | Input data | Disease type | Tools | Classifiers | Cross-validation | Accuracy |
| 1 | Padmanaban and Parthiban [8] | 2016 | Diabetic patients | CKD | WEKA, YALE | Naïve Bayes | 10 folds | 86% | UCI machine learning | Decision tree | 91% |
| 2 | Charleonnan et al. [9] | 2016 | Clinical data | CKD | WEKA, MATLAB | SVM | 5 folds | 98.3% | Logistic regression | 96.55% | Decision tree | 94.81% | KNN | 98.1% |
| 3 | Ghosh et al. [7] | 2020 | Apollo Hospitals India | CKD | Python | SVM | 5 folds | 99.56% | AB | 97.91% | LDA | 97.91% | GB | 99.80% |
| 4 | Fu et al.. [10] | 2018 | UCI repository (CKD dataset) | CKD | Python | RPART | No cross-validation | 98.2% | SVM | 97.3% | LOGR | 99.4% | MLP | 99.5% |
| 5 | Devika et al. [11] | 2019 | UCI repository (CKD dataset) | Chronic renal disorder | C Sharp | Naïve Bayes | No cross-validation | 99.63% | KNN | 87.78% | Random forest | 99.84% |
| 6 | Revathy et al. [12] | 2019 | UCI repository (CKD dataset) | CKD | Python | Decision tree | No cross-validation | 94.16% | SVM | 98.33% | Random forest | 99.16% |
| 7 | Nishat et al. [14] | 2021 | Learning repository of University of California, Irvine | CKD | Python | CNN | No cross-validation | 78% | LR | 98.25% | DT | 99% | RF | 99.75% | SVM | 85% | NB | 96.5% | MLP | 81.25% | QDA | 37.5% |
| 8 | Rabby et al. [13] | 2019 | UCI repository (CKD dataset) | CKD | Python | K-nearest neighbor | No cross-validation | 71.25% | RF | 98.75% | SVM | 97.50 | GNB | 100% | AB | 98.75% | DT | 100% | LDA | 97.50% | GB | 98.75 | LR | 97.50% | ANN | 65% |
| 9 | Pouriyeh et al. [15] | 2020 | UCI repository (CKD dataset) | CKD | Python | RF | 10 folds | 97.12% | ANN | 94.5% |
| 10 | Jabber et al. [16] | 2020 | UCI repository (CKD dataset) | CKD | Python | Decision tree | No cross-validation | 96.79% | Logistic regression | 97.86% | Naïve Bayes | 97.33% | Random forest | 98.93% |
| 11 | Bmc [17] | 2013 | UCI repository | Diabetic kidney disease | MATLAB | SVM | No cross-validation | 0.91 | PLS | 0.83 | FFNN | 0.85 | RPART | 0.87 | Random forest | 0.91 | Naïve Bayes | 0.86 | C5.0 | 0.90 |
| 12 | Ramya and Radha [18] | 2016 | UCI repository | Chronic kidney disease | R | BP | No cross-validation | 80.4 | RBF | 85.3 | Random forest (RF) | 78.6 |
| 13 | Kumar [19] | 2016 | UCI repository | CKD | MATLAB | RF | No cross-validation | 95.67 | SMO | 90 | Naïve Bayes | 87.64 | RBF | 83.78 | MLPC | 89 | SLG | 87 |
| 14 | Basarslan and Kayaalp [20] | 2019 | UCI repository | Chronic kidney disease | MATLAB | K-nearest neighbor | No cross-validation | 97 | Naïve Bayes | 96.5 | LR | 97.56 | RF | 99 |
| 15 | Dowluru and Rayavarapu [21] | 2012 | UCI repository | Kidney stone | WEKA tool | Naïve Bayes classification | No cross-validation | 0.99 | Logistic regression | 1.00 | J48 algorithm | 0.97 | Random forest | 0.98 | Orange tool | Naïve Bayes | | 0.79 | KNN | 0.7377 | Classification tree | 0.9352 | C4.5 | 0.9352 | SVM | 0.9198 | Random forest | 0.9352 |
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Bold values represent the highest accuracy in the relevant paper.
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