Research Article
Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner
Table 16
Comparison of the proposed work and existing work using clinical dataset.
| | Author/year | Method/reference | Accuracy % | | WDBC | SHD | HCC | HD | VCD | CHD | ILP |
| | Ayon et al. (2020) | DNN [45] | — | 98.15 | — | — | — | 94.39 | — | | SVM [45] | — | 97.41 | — | — | — | 97.36 | — | | Bai Ji et al. (2020) | IBPSO with -NN [46] | 96.14 | — | — | — | — | — | — | | Elgin et al. (2020) | Cooperative coevolution and RF [30] | 97.1 | 96.8 | 72.2 | 82.3 | 91.4 | 93.4 | — | | Magesh et al. (2020) | Cluster-based decision tree [47] | — | — | — | — | — | 89.30 | — | | Rabbi et al. (2020) | PCC and AdaBoost [48] | — | — | — | — | — | — | 92.19 | | Rajesh et al. (2020) | RF classifier [49] | — | — | 80.64 | — | — | — | — | | Salima et al. (2020) | ECSA with -NN [50] | 95.76 | 82.96 | — | — | — | — | — | | Singh J et al. (2020) | Logistic regression [51] | — | — | — | — | — | — | 74.36 | | Sreejith et al. (2020) | CMVO and RF [28] | — | — | — | — | — | — | 82.46 | | Sreejith et al. (2020) | DISON and ERT[27] | — | 94.5 | — | — | 87.17 | 93.67 | — | | Tougui et al. (2020) | ANN with Matlab [52] | — | — | — | — | — | 85.86 | — | | Tubishat et al. (2020) | ISSA with k-NN [53] | — | 88.1 | — | — | 89.0 | — | — | | Abdar et al. (2019) | Novel nested ensemble nu-SVC [54] | — | — | — | — | — | 98.60 | — | | Anter et al. (2019) | CFCSA with chaotic maps [31] | 98.6 | — | — | 68.0 | — | 88.0 | 68.4 | | Aouabed et al. (2019) | Nested ensemble nu-SVC, GA and multilevel balancing [55] | — | — | — | — | — | 98.34 | — | | Elgin et al. (2019) | DE, LO and GSO with Adaboost SVM [32] | 98.73 | — | — | 93.9 | — | — | — | | Książek et al. (2019) | SVM [56] | — | 97.41 | — | — | — | 97.36 | — | | Sayed et al. (2019) | Novel chaotic crow search algorithm with -NN [57] | 90.28 | 78.84 | — | 83.7 | — | — | 71.68 | | Abdar et al. (2018) | MPNN and C5.0 [58] | — | — | — | — | — | — | 94.12 | | Abdullah et al. (2018) | -NN [59] | — | — | — | — | 85.32 | — | — | | RF [59] | — | — | — | — | 79.57 | — | — | | Sawhney et al. (2018) | BFA and RF [60] | — | — | 83.50 | — | — | — | — | | Abdar et al. (2017) | Boosted C5.0 [61] | — | — | — | — | — | — | 93.75 | | CHAID [61] | — | — | — | — | — | — | 65.0 | | Zamani et al. (2016) | WOA with -NN [62] | — | 77.05 | — | 87.10 | — | — | — | | Abdar (2015) | SVM with rapid miner [63] | — | — | — | — | — | — | 72.54 | | C5.0 with IBM SPSS modeller [63] | — | — | — | — | — | — | 87.91 | | Santos et al. (2015) | Neural networks and augmented set approach [64] | — | — | 75.2 | — | — | — | — | | Chiu et al. (2013) | ANN and LR [65] | — | — | 85.10 | — | — | — | — | | Mauricio et al. (2013) | ABCO with SVM [66] | — | 84.81 | — | 87.10 | — | 83.17 | — | | Proposed | CSO, KH, BFO, and super learner | 96.83 | 86.36 | 94.74 | 90.48 | 81.82 | 84.00 | 70.00 |
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