Improved metaheuristics with machine learning enabled medical decision support system
Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnos...
- Autores:
-
Althubiti , Sara
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Soto-Diaz, Roosvel
Mansour, Romany F.
Alenezi, Fayadh
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9382
- Acceso en línea:
- https://hdl.handle.net/11323/9382
https://repositorio.cuc.edu.co/
- Palabra clave:
- Ovarian cancer
Decision support system
Smart healthcare
IoMT
Deep learning
Feature selection
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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dc.title.eng.fl_str_mv |
Improved metaheuristics with machine learning enabled medical decision support system |
title |
Improved metaheuristics with machine learning enabled medical decision support system |
spellingShingle |
Improved metaheuristics with machine learning enabled medical decision support system Ovarian cancer Decision support system Smart healthcare IoMT Deep learning Feature selection |
title_short |
Improved metaheuristics with machine learning enabled medical decision support system |
title_full |
Improved metaheuristics with machine learning enabled medical decision support system |
title_fullStr |
Improved metaheuristics with machine learning enabled medical decision support system |
title_full_unstemmed |
Improved metaheuristics with machine learning enabled medical decision support system |
title_sort |
Improved metaheuristics with machine learning enabled medical decision support system |
dc.creator.fl_str_mv |
Althubiti , Sara Escorcia-Gutierrez, Jose Gamarra, Margarita Soto-Diaz, Roosvel Mansour, Romany F. Alenezi, Fayadh |
dc.contributor.author.spa.fl_str_mv |
Althubiti , Sara Escorcia-Gutierrez, Jose Gamarra, Margarita Soto-Diaz, Roosvel Mansour, Romany F. Alenezi, Fayadh |
dc.subject.proposal.eng.fl_str_mv |
Ovarian cancer Decision support system Smart healthcare IoMT Deep learning Feature selection |
topic |
Ovarian cancer Decision support system Smart healthcare IoMT Deep learning Feature selection |
description |
Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black widow optimization with a machine learning-enabled decision support system (QBWO-MLDSS) for smart healthcare. The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately. Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data. In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets. Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance. The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty. The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches. |
publishDate |
2022 |
dc.date.accessioned.none.fl_str_mv |
2022-07-19T18:28:21Z |
dc.date.available.none.fl_str_mv |
2022-07-19T18:28:21Z |
dc.date.issued.none.fl_str_mv |
2022 |
dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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dc.type.redcol.spa.fl_str_mv |
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dc.identifier.citation.spa.fl_str_mv |
S. A. Althubiti, J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, R. F. Mansour et al., "Improved metaheuristics with machine learning enabled medical decision support system," Computers, Materials & Continua, vol. 73, no.2, pp. 2423–2439, 2022. |
dc.identifier.issn.spa.fl_str_mv |
1546-2218 |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/9382 |
dc.identifier.doi.spa.fl_str_mv |
10.32604/cmc.2022.028878 |
dc.identifier.eissn.spa.fl_str_mv |
1546-2226 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
S. A. Althubiti, J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, R. F. Mansour et al., "Improved metaheuristics with machine learning enabled medical decision support system," Computers, Materials & Continua, vol. 73, no.2, pp. 2423–2439, 2022. 1546-2218 10.32604/cmc.2022.028878 1546-2226 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/9382 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofjournal.spa.fl_str_mv |
Computers, Materials and Continua |
dc.relation.references.spa.fl_str_mv |
[1] M. W. L. Moreira, J. J. P. C. Rodrigues, V. Korotaev, J. Al-Muhtadi and N. Kumar, “A comprehensive review on smart decision support systems for health care,” IEEE Systems Journal, vol. 13, no. 3, pp. 3536–3545, 2019. [2] E. S. Kumar and P. S. Jayadev, “Deep learning for clinical decision support systems: A review from the panorama of smart healthcare,” Deep Learning Techniques for Biomedical and Health Informatics, Studies in Big Data Book Series, vol. 68, pp. 79–99, 2020. [3] W. Sun, G. Z. Dai, X. R. Zhang, X. Z. He and X. Chen, “TBE-Net: A three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–13, 2021. [4] W. Sun, L. Dai, X. R. Zhang, P. S. Chang and X. Z. He, “RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring,” Applied Intelligence, vol. 92, no. 6, pp. 1–16, 2021. [5] C. A. Kontovas, “The green ship routing and scheduling problem (GSRSP): A conceptual approach,” Transportation Research Part D: Transport and Environment, vol. 31, no. 3, pp. 61–69, 2014. [6] S. Ganguly, “Multi-objective distributed generation penetration planning with load model using particle SWARM optimization,” Decision Making: Applications in Management and Engineering, vol. 3, no. 1, pp. 30–42, 2020. [7] H. Engqvist, T. Z. Parris, J. Biermann, E. W. Rönnerman, P. Larsson et al., “Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes,” Scientific Reports, vol. 10, no. 1, pp. 1–13, 2020. [8] M. Akazawa and K. Hashimoto, “Artificial intelligence in ovarian cancer diagnosis,” Anticancer Research, vol. 40, no. 8, pp. 4795–4800, 2020. [9] R. F. Mansour, J. Escorcia-Gutierrez, M. Gamarra, V. García, D. Gupta et al., “Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images,” Neural Computing and Applications, vol. 11, no. 1, pp. 1–13, 2021. [10] K. Muthumayil, S. Manikandan, K. Srinivasan, J. Escorcia-Gutierrez, M. Gamarra et al., “Diagnosis of leukemia disease based on enhanced virtual neural network, Computers, Materials & Continua, vol. 69, no. 2, pp. 2031–2044, 2021. [11] J. Escorcia-Gutierrez, J. Torrents-Barrena, M. Gamarra, N. Madera, P. Romero-Aroca et al., “A feature selection strategy to optimize retinal vasculature segmentation,” Computers, Materials & Continua, vol. 70, no. 2, pp. 2971–2989, 2021. [12] J. Escorcia-Gutierrez, R. F. Mansour, K. Beleño, J. Jiménez-Cabas, M. Pérez et al., “Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images,”Computers, Materials & Continua, vol. 71, no. 2, pp. 4221–4235, 2022. [13] L. Zhang, J. Huang and L. Liu, “Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system,” Journal of MedicalSystems, vol. 43, no. 8, pp. 1–9, 2019. [14] M. Wu, C. Yan, H. Liu and Q. Liu, “Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks,” Bioscience Reports, vol. 38, no. 3, pp. 1–11, 2018. [15] A. Consiglio, G. Casalino, G. Castellano, G. Grillo, E. Perlino et al., “Explaining ovarian cancer gene expression profiles with fuzzy rules and genetic algorithms,” Electronics, vol. 10, no. 4, pp. 1–13, 2021. [16] J. H. Bae, M. Kim, J. S. Lim and Z. W. Geem, “Feature selection for colon cancer detection using k-means clustering and modified harmony search algorithm,” Mathematics, vol. 9, no. 5, pp. 1–14, 2021. [17] S. Sujamol, E. R. Vimina and U. Krishnakumar, “Improving recurrence prediction accuracy of ovarian cancer using multi-phase feature selection methodology,” Applied Artificial Intelligence, vol. 35, no. 3, pp. 206–226, 2021. [18] E. S. Paik, J. W. Lee, J. Y. Park, J. H. Kim, M. Kim et al., “Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods,” Journal of Gynecologic Oncology, vol. 30, no.4, pp. 1–13, 2019. [19] A. Arfiani and Z. Rustam, “Ovarian cancer data classification using bagging and random forest,” AIP Conference Proceedings, vol. 2168, no. 1, pp. 1–6, 2019. [20] M. Elhoseny, G. B. Bian, S. K. Lakshmanaprabu, K. Shankar, A. K. Singh et al., “Effective features to classify ovarian cancer data in internet of medical things,” Computer Networks, vol. 159, no. 17, pp. 147–156, 2019. [21] Y. E. Manzalawy, T. Y. Hsieh, M. Shivakumar, D. Kim and V. Honavar, “Min-redundancy and maxrelevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data,”BMC Medical Genomics, vol. 11, no. S3, pp. 20–31, 2018. [22] U. Ahmed, R. Mumtaz, H. Anwar, A. A. Shah, R. Irfan et al., “Efficient water quality prediction using supervised machine learning,” Water, vol. 11, no. 11, pp. 1–14, 2019. [23] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, no. 1, pp. 1–28, 2020. [24] L. T. Brady, C. L. Baldwin, A. Bapat, Y. Kharkov and A. V. Gorshkov, “Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems,” Physical Review Letters, vol. 126, no. 7, pp. 1–11, 2021. [25] V. B. Semwal, N. Gaud and G. C. Nandi, “Human gait state prediction using cellular automata and classification using ELM,” in Machine Intelligence and Signal Analysis, Advances in Intelligent Systems and Computing Book Series, vol. 748, Singapore: Springer, pp. 135–145, 2019. [26] A. E. Ezugwu and D. Prayogo, “Symbiotic organisms search algorithm: Theory, recent advances and applications,” Expert Systems with Applications, vol. 119, no. 6, pp. 184–209, 2019. |
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Althubiti , Sara Escorcia-Gutierrez, JoseGamarra, MargaritaSoto-Diaz, RoosvelMansour, Romany F.Alenezi, Fayadh2022-07-19T18:28:21Z2022-07-19T18:28:21Z2022S. A. Althubiti, J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, R. F. Mansour et al., "Improved metaheuristics with machine learning enabled medical decision support system," Computers, Materials & Continua, vol. 73, no.2, pp. 2423–2439, 2022.1546-2218https://hdl.handle.net/11323/938210.32604/cmc.2022.0288781546-2226Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black widow optimization with a machine learning-enabled decision support system (QBWO-MLDSS) for smart healthcare. The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately. Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data. In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets. Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance. The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty. The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.United States17 páginasapplication/pdfengTech Science PressAtribución 4.0 Internacional (CC BY 4.0)© 2020 Tech Science Presshttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Improved metaheuristics with machine learning enabled medical decision support systemArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.techscience.com/cmc/v73n2/48373Computers, Materials and Continua[1] M. W. L. Moreira, J. J. P. C. Rodrigues, V. Korotaev, J. Al-Muhtadi and N. Kumar, “A comprehensive review on smart decision support systems for health care,” IEEE Systems Journal, vol. 13, no. 3, pp. 3536–3545, 2019.[2] E. S. Kumar and P. S. Jayadev, “Deep learning for clinical decision support systems: A review from the panorama of smart healthcare,” Deep Learning Techniques for Biomedical and Health Informatics, Studies in Big Data Book Series, vol. 68, pp. 79–99, 2020.[3] W. Sun, G. Z. Dai, X. R. Zhang, X. Z. He and X. Chen, “TBE-Net: A three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–13, 2021.[4] W. Sun, L. Dai, X. R. Zhang, P. S. Chang and X. Z. He, “RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring,” Applied Intelligence, vol. 92, no. 6, pp. 1–16, 2021.[5] C. A. Kontovas, “The green ship routing and scheduling problem (GSRSP): A conceptual approach,” Transportation Research Part D: Transport and Environment, vol. 31, no. 3, pp. 61–69, 2014.[6] S. Ganguly, “Multi-objective distributed generation penetration planning with load model using particle SWARM optimization,” Decision Making: Applications in Management and Engineering, vol. 3, no. 1, pp. 30–42, 2020.[7] H. Engqvist, T. Z. Parris, J. Biermann, E. W. Rönnerman, P. Larsson et al., “Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes,” Scientific Reports, vol. 10, no. 1, pp. 1–13, 2020.[8] M. Akazawa and K. Hashimoto, “Artificial intelligence in ovarian cancer diagnosis,” Anticancer Research, vol. 40, no. 8, pp. 4795–4800, 2020.[9] R. F. Mansour, J. Escorcia-Gutierrez, M. Gamarra, V. García, D. Gupta et al., “Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images,” Neural Computing and Applications, vol. 11, no. 1, pp. 1–13, 2021.[10] K. Muthumayil, S. Manikandan, K. Srinivasan, J. Escorcia-Gutierrez, M. Gamarra et al., “Diagnosis of leukemia disease based on enhanced virtual neural network, Computers, Materials & Continua, vol. 69, no. 2, pp. 2031–2044, 2021.[11] J. Escorcia-Gutierrez, J. Torrents-Barrena, M. Gamarra, N. Madera, P. Romero-Aroca et al., “A feature selection strategy to optimize retinal vasculature segmentation,” Computers, Materials & Continua, vol. 70, no. 2, pp. 2971–2989, 2021.[12] J. Escorcia-Gutierrez, R. F. Mansour, K. Beleño, J. Jiménez-Cabas, M. Pérez et al., “Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images,”Computers, Materials & Continua, vol. 71, no. 2, pp. 4221–4235, 2022.[13] L. Zhang, J. Huang and L. Liu, “Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system,” Journal of MedicalSystems, vol. 43, no. 8, pp. 1–9, 2019.[14] M. Wu, C. Yan, H. Liu and Q. Liu, “Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks,” Bioscience Reports, vol. 38, no. 3, pp. 1–11, 2018.[15] A. Consiglio, G. Casalino, G. Castellano, G. Grillo, E. Perlino et al., “Explaining ovarian cancer gene expression profiles with fuzzy rules and genetic algorithms,” Electronics, vol. 10, no. 4, pp. 1–13, 2021.[16] J. H. Bae, M. Kim, J. S. Lim and Z. W. Geem, “Feature selection for colon cancer detection using k-means clustering and modified harmony search algorithm,” Mathematics, vol. 9, no. 5, pp. 1–14, 2021.[17] S. Sujamol, E. R. Vimina and U. Krishnakumar, “Improving recurrence prediction accuracy of ovarian cancer using multi-phase feature selection methodology,” Applied Artificial Intelligence, vol. 35, no. 3, pp. 206–226, 2021.[18] E. S. Paik, J. W. Lee, J. Y. Park, J. H. Kim, M. Kim et al., “Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods,” Journal of Gynecologic Oncology, vol. 30, no.4, pp. 1–13, 2019.[19] A. Arfiani and Z. Rustam, “Ovarian cancer data classification using bagging and random forest,” AIP Conference Proceedings, vol. 2168, no. 1, pp. 1–6, 2019.[20] M. Elhoseny, G. B. Bian, S. K. Lakshmanaprabu, K. Shankar, A. K. Singh et al., “Effective features to classify ovarian cancer data in internet of medical things,” Computer Networks, vol. 159, no. 17, pp. 147–156, 2019.[21] Y. E. Manzalawy, T. Y. Hsieh, M. Shivakumar, D. Kim and V. Honavar, “Min-redundancy and maxrelevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data,”BMC Medical Genomics, vol. 11, no. S3, pp. 20–31, 2018.[22] U. Ahmed, R. Mumtaz, H. Anwar, A. A. Shah, R. Irfan et al., “Efficient water quality prediction using supervised machine learning,” Water, vol. 11, no. 11, pp. 1–14, 2019.[23] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 87, no. 1, pp. 1–28, 2020.[24] L. T. Brady, C. L. Baldwin, A. Bapat, Y. Kharkov and A. V. Gorshkov, “Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems,” Physical Review Letters, vol. 126, no. 7, pp. 1–11, 2021.[25] V. B. Semwal, N. Gaud and G. C. Nandi, “Human gait state prediction using cellular automata and classification using ELM,” in Machine Intelligence and Signal Analysis, Advances in Intelligent Systems and Computing Book Series, vol. 748, Singapore: Springer, pp. 135–145, 2019.[26] A. E. Ezugwu and D. Prayogo, “Symbiotic organisms search algorithm: Theory, recent advances and applications,” Expert Systems with Applications, vol. 119, no. 6, pp. 184–209, 2019.24392423273Ovarian cancerDecision support systemSmart healthcareIoMTDeep learningFeature selectionPublicationORIGINALImproved Metaheuristics with Machine Learning Enabled Medical Decision.pdfImproved Metaheuristics with Machine Learning Enabled Medical Decision.pdfapplication/pdf1241077https://repositorio.cuc.edu.co/bitstreams/d456cfb8-76ea-4ec3-bc91-6de82757cabd/downloadec98ecade7b007386c81995a0d871695MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/c834b644-ecf5-44d5-94be-eb67b645b7cc/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTImproved Metaheuristics with Machine Learning Enabled Medical Decision.pdf.txtImproved Metaheuristics with Machine Learning Enabled Medical 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