Advanced Cancer Classification Using AI and Pattern Recognition Techniques
Accurate cancer classification is essential for early detection and effective treatment, yet the complexity of gene expression presents significant challenges. In this study, we explored how combining multiple feature selection methods with various classifiers enhances the identification of marker g...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_02001.pdf |
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| _version_ | 1841554703430713344 |
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| author | Haddou Bouazza Sara Haddou Bouazza Jihad |
| author_facet | Haddou Bouazza Sara Haddou Bouazza Jihad |
| author_sort | Haddou Bouazza Sara |
| collection | DOAJ |
| description | Accurate cancer classification is essential for early detection and effective treatment, yet the complexity of gene expression presents significant challenges. In this study, we explored how combining multiple feature selection methods with various classifiers enhances the identification of marker genes for four cancers: leukemia, lung, lymphoma, and ovarian cancer. We applied feature selection techniques such as the F Test, Signal-to-Noise Ratio (SNR), T-test, ReliefF, Correlation Coefficient, Mutual Information, and minimum redundancy maximum relevance, along with classifiers including K-Nearest Neighbors, Support Vector Machines, Linear Discriminant Analysis, Decision Tree Classifiers, and Naive Bayes. Our results demonstrate that the SNR method consistently achieved the highest accuracy in gene selection, particularly when paired with K-means clustering. Remarkably, leukemia was classified with 100% accuracy using only four genes, lung cancer, and lymphoma with 100% and 97% accuracy, respectively, using three genes, and ovarian cancer with 100% accuracy using just one gene. These findings highlight the potential of minimal gene sets for highly precise cancer classification. |
| format | Article |
| id | doaj-art-5ccab0fc9fca48ce9a4682e8e932ae6f |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-5ccab0fc9fca48ce9a4682e8e932ae6f2025-01-08T10:58:54ZengEDP SciencesITM Web of Conferences2271-20972024-01-01690200110.1051/itmconf/20246902001itmconf_maih2024_02001Advanced Cancer Classification Using AI and Pattern Recognition TechniquesHaddou Bouazza Sara0Haddou Bouazza Jihad1LAMIGEP, EMSILAMIGEP, EMSIAccurate cancer classification is essential for early detection and effective treatment, yet the complexity of gene expression presents significant challenges. In this study, we explored how combining multiple feature selection methods with various classifiers enhances the identification of marker genes for four cancers: leukemia, lung, lymphoma, and ovarian cancer. We applied feature selection techniques such as the F Test, Signal-to-Noise Ratio (SNR), T-test, ReliefF, Correlation Coefficient, Mutual Information, and minimum redundancy maximum relevance, along with classifiers including K-Nearest Neighbors, Support Vector Machines, Linear Discriminant Analysis, Decision Tree Classifiers, and Naive Bayes. Our results demonstrate that the SNR method consistently achieved the highest accuracy in gene selection, particularly when paired with K-means clustering. Remarkably, leukemia was classified with 100% accuracy using only four genes, lung cancer, and lymphoma with 100% and 97% accuracy, respectively, using three genes, and ovarian cancer with 100% accuracy using just one gene. These findings highlight the potential of minimal gene sets for highly precise cancer classification.https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_02001.pdf |
| spellingShingle | Haddou Bouazza Sara Haddou Bouazza Jihad Advanced Cancer Classification Using AI and Pattern Recognition Techniques ITM Web of Conferences |
| title | Advanced Cancer Classification Using AI and Pattern Recognition Techniques |
| title_full | Advanced Cancer Classification Using AI and Pattern Recognition Techniques |
| title_fullStr | Advanced Cancer Classification Using AI and Pattern Recognition Techniques |
| title_full_unstemmed | Advanced Cancer Classification Using AI and Pattern Recognition Techniques |
| title_short | Advanced Cancer Classification Using AI and Pattern Recognition Techniques |
| title_sort | advanced cancer classification using ai and pattern recognition techniques |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_02001.pdf |
| work_keys_str_mv | AT haddoubouazzasara advancedcancerclassificationusingaiandpatternrecognitiontechniques AT haddoubouazzajihad advancedcancerclassificationusingaiandpatternrecognitiontechniques |