Improved metaheuristics with machine learning enabled medical decision support system

dc.creatorAlthubiti , Sara
dc.creatorEscorcia-Gutierrez, Jose
dc.creatorGamarra, Margarita
dc.creatorSoto-Diaz, Roosvel
dc.creatorMansour, Romany F.
dc.creatorAlenezi, Fayadh
dc.date2022-07-19T18:28:21Z
dc.date2022-07-19T18:28:21Z
dc.date2022
dc.date.accessioned2023-10-03T19:27:56Z
dc.date.available2023-10-03T19:27:56Z
dc.identifierS. 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.identifier1546-2218
dc.identifierhttps://hdl.handle.net/11323/9382
dc.identifier10.32604/cmc.2022.028878
dc.identifier1546-2226
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170232
dc.descriptionSmart 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.
dc.descriptionUnited States
dc.format17 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherTech Science Press
dc.relationComputers, Materials and Continua
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[8] M. Akazawa and K. Hashimoto, “Artificial intelligence in ovarian cancer diagnosis,” Anticancer Research, vol. 40, no. 8, pp. 4795–4800, 2020.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation2439
dc.relation2423
dc.relation2
dc.relation73
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rights© 2020 Tech Science Press
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.techscience.com/cmc/v73n2/48373
dc.subjectOvarian cancer
dc.subjectDecision support system
dc.subjectSmart healthcare
dc.subjectIoMT
dc.subjectDeep learning
dc.subjectFeature selection
dc.titleImproved metaheuristics with machine learning enabled medical decision support system
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


Este ítem pertenece a la siguiente institución