Table 1 Classification accuracy of different methods with literature

Saxena et al. [6]

Feature selection outlier rejection missing value padding

K-nearest neighbor, Random forest

79.80

Krishnamoorthi et al. [7]

Missing value processing, outlier removal, normalization

Logistic regression

83.00

Butt et al. [8]

Various classifiers and models

Random forest, multilayer perceptron, LSTM

86

Garcia-Ordas et al. [13]

Variational self-encoder, sparse self-encoder

Convolutional neural network, sparse self-encoder

92.31

Bukhari et al. [15]

No data preprocessing

Artificial back propagation proportional conjugate gradient neural network (ABP-SCGNN)

93

Gnanadass [18]

Missing data filling (mean)

Naive Bayes, linear regression, random forest, AdaBoost gradient boosting machine, extreme gradient boosting

78

Maniruzzaman et al. [10]

Missing data and outlier handling feature extraction and optimization

Ten different classifiers

92.26

Zou et al. [9]

Dimensionality reduction (PCA, mRMR)

Decision trees, random forests, neural networks

80.84

Hayashi and Yukita [19]

Rule extraction algorithm, sampling selection technique

J48 graft, rule extraction

83.83

Alneamy et al. [20]

TLBO algorithm, hybrid fuzzy wavelet neural network

Functional fuzzy wavelet neural network (FFWNN)

88.67

Maniruzzaman et al. [11]

Gaussian Process-based classification, three kernel functions

Gaussian process, LDA, QDA, NB

81.97

Joshi and Dhakal [12]

Logistic regression, decision tree

Logistic regression, decision tree

78.26

Ejiyi et al. [22]

Data augmentation, attribute analysis missing data imputations

XGBoost, adaboost

94.67

Rahman et al. [16]

Convolutional long short-term memory

Conv-LSTM, CNN, T-LSTM, CNN-LSTM

91.38

Rehman et al. [17]

Handling Miss values, moving average normalization

Deep extreme learning machine (DELM)

92.80