Table 1 Review of previous work.
Sl.No. | Author and year | Database | Feature extraction/dimensionality reduction technique | Classifiers used | Evaluation metrics |
|---|---|---|---|---|---|
1 | Liu et al. (2013)16 | Colorectal cancer Dataset (Prof.Lindy Durant’s research) | Continuous Wavelet Transform (CWT) | Genetic Algorithm based on Bayes classifier | Accuracy = 78.6% |
2 | Islam et al. (2023)17 | Ischemic sensitivity dataset | genomap | genomap + genoNet | Accuracy = 93% |
3 | Xiao et al. (2018)18 | TCGA Database | Stacked sparse auto-encoder (SSAE) | Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF) | Accuracy = 99.89% |
4 | You et.al. (2013)19 | (Bio) Dataset | F-test & Partial Least Squares (PLS), ReliefF and PLS, Recursive Feature Elimination and PLS | LDA,SVM, NBC,KNN (K-Nearest Neighbourhood), INN | 96.43% |
5 | Bonev et al. (2008)20 | Datasets from Broad Institute, Stanford Genomic Resources, and Princeton University | Mutual Information | KNN, SVM | Accuracy = 89% |
6 | Xu et al. (2018)21 | Simulated Genome Dataset Available in Bioinformatics online | Extreme phenotype sampling (EPS) | LASSO | Accuracy = 90% |
7 | Torkey et al. (2021)22 | METABRIC, Nature 2012, and Nat Commun 2016, COX-PASnet, and TCGA | Principal Component Analysis (PCA) with Sparse Autoencoders | Cox Regression, Random Survival Forest (RSF) | Accuracy = 98% |
8 | Abdulla et al. (2020)23 | Leukemia and DLBCL dataset | Genetic Algorithm (GA) and Binary Gray Wolf Optimization (BGWO) | Random Forest (RF) and KNN | Accuracy = 95% |
9 | Li et al. (2018)24 | Colon cancer and Leukemia dataset | Variants of LDA, LASSO Spectral regression discriminant analysis (SRDA), locality preserving projections (LPP), kernel discriminant analysis with spectral graph analysis (SRKDA) | LDA, RLDA, HLDA,NDA, SRDA, LPP, SRKDA, and Lasso SRDA | Accuracy = 84% |
10 | Zhang et al. (2015)25 | Leukemia-ALLAML, SRBCT | Projection Matrix | kNN, Locally Linear Discriminant Embedding (LLDE) , DNE (Discriminant Neighbourhood Embedding) | Accuracy = 90% |
11 | Subhajit et al. (2015)26 | SRBCT, ALL_AML and MLL microarray datasets | Particle Swarm Optimization (PSO)—adaptive KNN | SVM (Linear, RBF, Polynomial, Quadratic) | Accuracy = 95% |
12 | Nursabillilah et al. (2022)27 | Breast Cancer Gene Expression Database | Information Gain, Relief and Fisher Score (filter-based methods), LASSO (embedded-based method), GA, PSO, harmony search, ant colony, artificial bee colony, firefly algorithm, cuckoo search, gravitational search, grey wolf, whale optimization | RF, KNN, Naïve Bayes (NB), Logistic Regression (LR), Fuzzy Logic, Artificial NN (ANN) | Accuracy = 90% to 100% |
13 | Wang et al. (2003)28 | Leukemia, Colon Cancer, Brain tumours and NCI60 | Fuzzy c-means clustering, Weighted/Mean component plane | Fisher's linear discriminant | Accuracy = 95% |
14 | Aziz et al. (2016)29 | colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma | Independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) | SVM and NB | Accuracy = 90% |
15 | Yaqoob et al. (2024)30 | Breast cancer dataset (Kent Ridge) | Sine Cosine and Cuckoo Search Algorithm | SVM | Accuracy = 99% |
16 | Joshi et al. (2024)31 | Colon cancer | Cuckoo Search and Spider Monkey Optimization | SVM and NB | Accuracy = 92% |
