Table 2.

Review of the studies, data sources, their purpose, and machine-learning algorithms reported from 2001 to 2015.

	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, k-nearest neighbor, self-organizing map, and support vector machine. Majority voting, weighted voting, and Bayesian approach
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 (K-nearest neighbor), logistic regression, and neural networks
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 K-nearest neighbor (K-NN) classifier
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, feature selection in recursive and nonrecursive settings
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	t-statistics to rank the gene and support vector machines
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	p value and clustering
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, linear discriminant function and SVM, and hierarchical clustering method
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 support system preprocessing, filtering, classification, and extraction of knowledge	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	K-nearest neighbor technique
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), k-nearest neighbor (k-NN), and SVM
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 K-nearest neighborhood (KNN)
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