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In the first stage, an overall ranker is initiated to combine the results of three filter-based feature evaluation methods, namely, chi-squared, F-statistic, and mutual information (MI). The features are then ordered according to this combination. In the second stage, the modified wrapper-based sequential forward selection is utilized to discover the optimal feature subset, using ML models such as support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) classifiers. To examine the proposed algorithm, many tests have been carried out on four cancerous microarray datasets, employing in the process 10-fold cross-validation and hyperparameter tuning. The performance of the algorithm is evaluated by calculating the diagnostic accuracy. The results indicate that for the leukemia dataset, both SVM and KNN models register the highest accuracy at 100% using only 5 features. For the ovarian cancer dataset, the SVM model achieves the highest accuracy at 100% using only 6 features. For the small round blue cell tumor (SRBCT) dataset, the SVM model also achieves the highest accuracy at 100% using only 8 features. For the lung cancer dataset, the SVM model also achieves the highest accuracy at 99.57% using 19 features. By comparing with other algorithms, the results obtained from the proposed algorithm are superior in terms of the number of selected features and diagnostic accuracy.<\/jats:p>","DOI":"10.1155\/2022\/1056490","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T17:35:05Z","timestamp":1660066505000},"page":"1-15","source":"Crossref","is-referenced-by-count":20,"title":["A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9302-2019","authenticated-orcid":true,"given":"Tarneem","family":"Elemam","sequence":"first","affiliation":[{"name":"Information Systems Department, Suez Canal University, Ismailia 41522, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9777-8086","authenticated-orcid":true,"given":"Mohamed","family":"Elshrkawey","sequence":"additional","affiliation":[{"name":"Information Systems Department, Suez Canal University, Ismailia 41522, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114012","article-title":"Gene selection and classification of microarray data method based on mutual information and moth flame algorithm","volume":"166","author":"A. 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