Study Year Tasks Data source Leukaemia types involved in the study Purpose Methods 1 Cho [82 ] 2002 Feature selection and classification DNA microarray AML, ALL Classifying leukaemia types Pearson’s and Spearman’s correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal-to-noise ratio being used for feature selection 2 Inza et al. [83 ] 2002 Feature selection and classification DNA microarray AML, ALL Classifying cancer, select genes related to cancer Feature subset selection, case-based, and nearest neighbor classifier 3 Farag [84 ] 2003 Feature selection and classification Blood cells image AML, ALL Classifying leukaemia types A three-layer backpropagation neural network 4 Futschik et al. [85 ] 2003 Knowledge discovery Gene expression AML, ALL Classifying leukaemia types and select gene expression Knowledge-based neural networks and evolving fuzzy neural networks and adaptive learning and rule extraction 5 Cho and Won [86 ] 2003 Feature selection, classification, and ensemble classifiers DNA microarray AML, ALL Classifying leukaemia types and select genes related to cancer Correlation coefficient, Euclidean distance, cosine coefficient, information gain, mutual information, a feed-forward multilayer perceptron, 6 Marx et al. [44 ] 2003 Feature selection and classification DNA microarray AML, ALL Classifying leukaemia from nonleukaemia Principal component analysis and clustering 7 Marohnic et al. [87 ] 2004 Feature selection and classification DNA microarray AML, ALL Classifying leukaemia types Mutual information and support vector machine 8 McCarthy et al. [88 ] 2004 Knowledge extraction, classification, feature selection, visualization Proteomic mass spectroscopy data, and gene expression Melanoma, leukaemia Cancer detection, diagnosis, and management Naïve Bayes, support vector machines, instance-based learning ( 9 Rowland [89 ] 2004 Classification Gene expression AML, ALL Classifying leukaemia types Genetic Programming 10 Markiewicz et al. [90 ] 2005 Feature selection and classification Images of different blast cell Myelogenous leukaemia Classifying patients Support vector machine 11 Tung and Quek [91 ] 2005 Classification DNA microarrays ALL Classifying leukaemia types A neural fuzzy system, NN, SVM and the 12 Nguyen et al. [92 ] 2005 Classification DNA microarrays AML, ALL Classifying leukaemia types Support vector machine (SVM) 13 Plagianakos et al. [93 ] 2005 Feature selection and classification DNA microarrays AML, ALL Classifying leukaemia types artificial neural networks 14 Li and Yang [94 ] 2005 Feature selection and classification DNA microarrays AML, ALL Classifying leukaemia types SVM, ridge regression and Rocchio, 15 Jinlian et al. [95 ] 2005 Knowledge extraction DNA microarray AML, ALL Leukaemia gene association structure Clusters 16 Diaz et al. [96 ] 2006 Feature selection and classification DNA microarrays Acute Promyelocytic Leukaemia Classifying Acute Promyelocytic Leukaemia (APL) from the non-APL leukaemia Discriminant fuzzy pattern 17 Feng and Lipo [97 ] 2006 Feature selection and classification DNA microarrays AML, ALL Acute leukaemia types 18 Nguyen and Ohn [98 ] 2006 Feature selection and classification DNA microarrays AML, ALL Classifying leukaemia types Dynamic recursive feature elimination and random forest 19 Shulin et al. [99 ] 2006 Feature selection and classification DNA microarrays AML, ALL Classifying leukaemia types Independent component analysis and SVM 20 Chen et al. [100 ] 2007 Feature selection, rule extraction, and classification DNA microarrays AML, ALL Classifying leukaemia types A multiple kernel support vector machine 21 Ujwal et al. [43 ] 2007 Feature selection and classification DNA microarray ALL Identifying functional cancer cell line classes, classifying leukaemia from nonleukaemia 22 Perez et al. [101 ] 2008 Classification Gene expression AML, ALL Classify leukaemia types Hybrid fuzzy-SVM 23 Yoo and Gernaey [42 ] 2008 Feature selection and classification DNA microarrays data ALL Classifying ALL origin cell lines from non-ALL leukaemia origin cell lines Discriminant partial least squares, principal component and Fisher’s linear discriminant analysis, 24 Avogadri et al. [102 ] 2009 Knowledge extraction Gene expression Myeloid leukaemia Discovering significant clusters Stability-based methods 25 Eisele et al. [49 ] 2009 Knowledge extraction Gene expression CLL Prognostic markers Multivariate model 26 Chaiboonchoe et al. [103 ] 2009 Classification DNA microarrays data ALL Identification of differentially expressed genes Self-organizing maps (neural networks), emergent self-organizing maps (extension of neural networks), the short-time series expression miner (STEM), and fuzzy clustering by local approximation of membership (FLAME) 27 Oehler et al. [46 ] 2009 Knowledge extraction Gene expression CML Identifying molecular markers Bayesian model averaging 28 Corchado et al. [45 ] 2009 Decision Exon arrays ALL, AML, CLL, CML Classifying patients who suffer from different forms of leukaemia at various stages Principal components, clustering, CART 29 Glez-Peña et al. [104 ] 2009 Feature selection and classification DNA microarray AML Classifying gene expression Fuzzy pattern algorithm 30 He and Hui [105 ] 2009 Classification DNA microarray ALL, AML Classifying leukaemia types Ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms 31 Mukhopadhyay et al. [106 ] 2009 Feature selection and classification DNA microarray ALL, AML Classifying leukaemia types GA-based fuzzy clustering, neural network, and support vector machine 32 Torkaman et al. [107 ] 2009 Classification Human leukaemia tissue ALL, AML Determining different CD markers Cooperative game 33 Zheng et al. [108 ] 2009 Feature selection DNA microarray ALL Gene ranking Knowledge-oriented gene selection 34 Mehdi et al. [109 ] 2009 Knowledge acquisition Gene expression ALL, AML Pattern clustering 35 Porzelius et al. [110 ] 2011 Feature selection, classification Microarray and clinical data ALL Risk prediction Feature selection approach for support vector machines as well as a boosting approach for regression models 36 Chen et al. [111 ] 2011 Feature selection, data fusion, class prediction, decision rule extraction, associated rule extraction, and subclass discovery DNA microarray ALL, AML Select gene, classify leukaemia types, rule extraction Multiple kernel SVM 37 Gonzalez et al. [112 ] 2011 Classification Bone marrow cells images ALL, AML Classifying leukaemia subtypes Segmentation method to obtain leukaemia cells and extract from them descriptive characteristics (geometrical, texture, statistical) and eigenvalues 38 Tong and Schierz [113 ] 2011 Feature selection and classification DNA microarray ALL, AML Classifying two-class oligonucleotide microarray data for acute leukaemia Hybrid genetic algorithm-neural network 39 Chauhan et al. [114 ] 2012 Classification Genotype ALL, AML Identifying gene-gene interaction Classification and regression tree 40 Escalante et al. [115 ] 2012 Feature selection and classification The morphological properties of bone marrow images ALL, AML Classifying leukaemia subtypes Ensemble particle swarm model selection 41 Yeung et al. [116 ] 2012 Feature selection and classification Gene expression CML select gene, and predicted functional relationships Integrating gene expression data with expert knowledge and predicted functional relationships using iterative Bayesian model averaging 42 Manninen et al. [117 ] 2013 Classification Flow cytometry data AML Prediction method for diagnosis of AML Sparse logistic regression 43 El-Nasser et al. [118 ] 2014 Classification DNA microarrays ALL, AML Classifying leukaemia types Implement enhanced classification (ECA) algorithm, SMIG module, and ranking procedure. 44 Singhal and Singh [119 ] 2015 Feature selection and classification Image based analysis of bone marrow samples ALL Classifying leukaemia subtypes Multilayer perceptron (MLP), linear vector quantization (LVQ), 45 Yao et al. [120 ] 2015 Feature selection and classification DNA microarrays ALL, AML, the mixed-lineage leukaemia (MLL) data Classifying leukaemia subtypes Random forests and ranking features 46 Rawat et al. [121 ] 2015 Computer-aided diagnostic system, feature selection, and classification Bone marrow cells in microscopic images ALL Diagnosis lymphoblast cells from healthy lymphocytes Support vector machine 47 Kar et al. [122 ] 2015 Feature selection and classification DNA microarrays ALL, AML, the mixed-lineage leukaemia (MLL) data Classifying leukaemia subtypes Particle swarm optimization (PSO) method along with adaptive 48 Li et al. [123 ] 2016 Classification Gene expression AML Identifying feature genes Support vector machine (SVM) and random forest (RF) 49 Dwivedi et al. [124 ] 2016 Classification Microarray gene expression ALL, AML Classifying leukaemia subtypes Artificial neural network (ANN) 50 Krappe et al. [125 ] 2016 Classification Image based analysis of bone marrow samples Leukaemia Diagnosis of leukaemia and classifying 16 different classes for bone marrow Knowledge-based hierarchical tree classifier 51 Li et al. [123 ] 2016 Classification DNA microarrays AML, ALL Classifying leukaemia subtypes A weighted doubly regularized support vector machine 52 Ocampo-Vega et al. [126 ] 2016 Feature selection and classification DNA microarrays AML, ALL Classifying leukaemia subtypes Principal component analysis and logistic regression 53 Rajwa et al. [127 ] 2016 Classification Flow cytometry data AML Determining progression of the disease Nonparametric Bayesian framework 54 Ni et al. [128 ] 2016 Classification Flow cytometry data AML Analyzing minimal residual disease Support vector machines (SVM) 55 Savvopoulos et al. [48 ] 2016 Knowledge extraction CLL cells in peripheral blood CLL Capturing disease pathophysiology across patient types Temporally and spatially distributed model
