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Table 1 Summary of some related works

From: Chronic kidney disease prediction using machine learning techniques

No.

Author

Technique applied

Claimed outcome

Draw back

1

Salekin and Stankovic [9]

K-NN, RF, and NN, Wrapper approach and Embedded approach

Detection F1-score of RF 99.8

Small dataset size with missing values was used; Severity level prediction was not included

2

Tekale et al. [10]

DT and SVM

DT and SVM with an accuracy of 91.75 and 96.75 respectively

Dataset size need to increased, Severity level prediction was not included. Only to classifiers result compared

3

Priyanka et al. [12]

NB, KNN, SVM, DT, and ANN. NB

NB, KNN, SVM, DT, and ANN. NB accuracy is 94.6%

Small size dataset. No stages prediction. Feature extraction was not carried out and classification accuracy needs improvement

4

Yashfi [14]

RF and ANN

RF and ANN with an accuracy of 97.12% and 94.5%

Small size dataset and no stages prediction

5

Rady and Anwar [15]

PNN, MLP, SVM, RBF

PNN, MLP, SVM, RBF accuracy of 96.7, 60.7, 87, and 51.5 respectively

Small data size. the algorithms, which is not appropriate for small dataset size were used

6

Alsuhibany et al. [16]

EDL-CDSS technique, ADASYN based outlier detection and QOBOA based hyperparameter tuning

The proposed EDL-CDSS method has depicted the other approaches with the superior sensy and specy of 0.9680 and 0. 9702 compared to ACO, FNC, KELM, CNN-GRU, DBN, DT, MLP and D-ACO

The size of benchmark dataset is small. IoT and benchmark data integration is not clearly explained. The study focus only on binary classification (ckd or not-ckd)

7

Poonia et al. [17]

KNN, ANN, SVM, NB and LR with Chi-Square and RFE

KNN, ANN, SVM, NB, LR accuracy of 66.25%,65%,97.5%,95%,97.5% respectively

Small size dataset and no stages prediction. Accuracy needs improvement