A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction
In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial p...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024014002 |
| _version_ | 1797304497950687232 |
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| author | Erum Yousef Abbasi Zhongliang Deng Qasim Ali Adil Khan Asadullah Shaikh Mana Saleh Al Reshan Adel Sulaiman Hani Alshahrani |
| author_facet | Erum Yousef Abbasi Zhongliang Deng Qasim Ali Adil Khan Asadullah Shaikh Mana Saleh Al Reshan Adel Sulaiman Hani Alshahrani |
| author_sort | Erum Yousef Abbasi |
| collection | DOAJ |
| description | In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care. |
| first_indexed | 2024-03-08T00:10:23Z |
| format | Article |
| id | doaj.art-ac98a200559247d6b7a28b07022d5d4c |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T00:10:23Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ac98a200559247d6b7a28b07022d5d4c2024-02-17T06:40:53ZengElsevierHeliyon2405-84402024-02-01103e25369A machine learning and deep learning-based integrated multi-omics technique for leukemia predictionErum Yousef Abbasi0Zhongliang Deng1Qasim Ali2Adil Khan3Asadullah Shaikh4Mana Saleh Al Reshan5Adel Sulaiman6Hani Alshahrani7State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, PakistanState Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Scientific and Engineering Research Centre, Najran University, Najran, 61441, Saudi Arabia; Corresponding author. Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi ArabiaIn recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.http://www.sciencedirect.com/science/article/pii/S2405844024014002Multi-omicsGenomicsMachine learningDeep learningLeukemia |
| spellingShingle | Erum Yousef Abbasi Zhongliang Deng Qasim Ali Adil Khan Asadullah Shaikh Mana Saleh Al Reshan Adel Sulaiman Hani Alshahrani A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction Heliyon Multi-omics Genomics Machine learning Deep learning Leukemia |
| title | A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction |
| title_full | A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction |
| title_fullStr | A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction |
| title_full_unstemmed | A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction |
| title_short | A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction |
| title_sort | machine learning and deep learning based integrated multi omics technique for leukemia prediction |
| topic | Multi-omics Genomics Machine learning Deep learning Leukemia |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024014002 |
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