Table 1.

Summary of the systematic analysis studies in related work.

Study	Models	Dataset	Evaluation metrics	Results
15	Bayes Network learning, Conjunctive Rule, NBTree, VFI, Random Subspace, Naïve Bayes Updateable, and PART	Three datasets contains 7130 Genes	Accuracy	97.22% for 500 genes
4	Local Directional path	90 high-quality $184\times 138$ size images obtained from the American Society of Hematology	Sensitivity, Specificity, Precision, F-Measure	Sensitivity: 100%, Specificity: 80%, Precision: 85.74%, F-Measure: 93.4%
6	K-Means, Fuzzy C Means, Weighted K Means	Heart dataset from UCI machine learning repository	Cluster accuracy, error rate and execution time	Leukemia, K-Means: 78%, Fuzzy means: 75%, WK-Means: 85%
13	Updatable NB, MLP, KNN, SVM	25 variables or features and 82 instances or records	Accuracy	NB 94.76%, MLP 95.24%, SVM 96.20%, KNN 91.43%
16	Fuzzy c-means clustering, PCA, SVM	21 peripheral blood smear and bone marrow slides of 14 patients with all and 7 normal persons $2592\times 3872$ pixels in red green blue (RGB) color	sensitivity, specificity, accuracy, precision and false negative	Sensitivity 98%, Specificity 97%, Accuracy 98%, Precision 98%
17	Linde–Buzo–Gray, Kekre’s Propotionate Error, K-Means	115 digital images of size $256\times 256$. 16 datasets with 2415 images, 642 images with size $632\times 480$ pixels	Sensitivity, specificity, accuracy	Sensitivity 100%, Specificity 99.747%, Accuracy 99.7617%
7	KNN, SVM, DT, RF, GBDT	Three RNA-seq data sets	Precision, recall and accuracy	Accuracy LUAD: 98.80 (± 1.79), STAD: 98.78 (± 1.44), BRCA: 98.41 (± 0.41)
12	Deep convolutional neural networks	Images from ALL-Image DataBase (IDB)	Sensitivity, specificity, accuracy	Sensitivity 100%, Specificity 98.11%, Accuracy of 99.50%
14	AlexNet	2,820 images	Precision, Recall, accuracy	100% classification accuracy
18	Alert Net-RWD	16 datasets with 2,415 images	Accuracy, precision	Accuracy 97.18%, Precision 97.23%
19	SVM, KNN, NB, and RF	NCBI/GEO public database: 11 series from Microarray and 2 series from RNA-seq	ANOVA statistical test, accuracy, F1	10 Genes F1-score: SVM: 97.13%, KNN: 96.28%, NB: 97.29%, RF: 97.01%
20	DNN deep learning network	36 cases containing 22,283 gene expression of acute myeloid leukemia (AML) microarray	Accuracy	Accuracy: 96.6%