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Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine

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31 jul 2025

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Lupat, R., Perera, R., Loi, S., Li, J. (2023). Moanna: Multi-omics autoencoder-based neural network algorithm for predicting breast cancer subtypes. IEEE Access, 11, 10912–10924. https://doi.org/10.1109/ACCESS.2023.3240515 LupatR. PereraR. LoiS. LiJ. 2023 Moanna: Multi-omics autoencoder-based neural network algorithm for predicting breast cancer subtypes IEEE Access 11 10912 10924 https://doi.org/10.1109/ACCESS.2023.3240515 Search in Google Scholar

Megha, R., Geethapriya, Radhakrishna, S., Eranki, A. (2024). Breast tumor heterogeneity quantification using 3D ultrasound texture. In 2024 IEEE South Asian Ultrasonics Symposium (SAUS). IEEE. https://doi.org/10.1109/SAUS61785.2024.10563639 MeghaR. Geethapriya RadhakrishnaS. ErankiA. 2024 Breast tumor heterogeneity quantification using 3D ultrasound texture In 2024 IEEE South Asian Ultrasonics Symposium (SAUS) IEEE https://doi.org/10.1109/SAUS61785.2024.10563639 Search in Google Scholar

Mo, Y., Han, C., Liu, Y., Liu, M., Shi, Z., Lin, J. (2023). HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free breast cancer diagnosis in ultrasound images. IEEE Transactions on Medical Imaging, 42 (6), 1696–1706. https://doi.org/10.1109/TMI.2023.3236011 MoY. HanC. LiuY. LiuM. ShiZ. LinJ. 2023 HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free breast cancer diagnosis in ultrasound images IEEE Transactions on Medical Imaging 42 6 1696 1706 https://doi.org/10.1109/TMI.2023.3236011 Search in Google Scholar

Lamprou, C., Katsikari, K., Rahmani, N., Hadjileontiadis, L. J., Seghier, M., Alshehhi, A. (2024). StethoNet: Robust breast cancer mammography classification framework. IEEE Access, 12, 144890–144904. https://doi.org/10.1109/ACCESS.2024.3473010 LamprouC. KatsikariK. RahmaniN. HadjileontiadisL. J. SeghierM. AlshehhiA. 2024 StethoNet: Robust breast cancer mammography classification framework IEEE Access 12 144890 144904 https://doi.org/10.1109/ACCESS.2024.3473010 Search in Google Scholar

Felício, J. M., Martins, R. A., Costa, J. R., Fernandes, C. A. (2024). Microwave breast imaging for cancer diagnosis: An overview [Bioelectromagnetics]. IEEE Antennas and Propagation Magazine, 66 (4), 85–97. https://doi.org/10.1109/MAP.2024.3411480 FelícioJ. M. MartinsR. A. CostaJ. R. FernandesC. A. 2024 Microwave breast imaging for cancer diagnosis: An overview [Bioelectromagnetics] IEEE Antennas and Propagation Magazine 66 4 85 97 https://doi.org/10.1109/MAP.2024.3411480 Search in Google Scholar

Prabakaran, D., Sheela, K. (2021). A strong authentication for fortifying wireless healthcare sensor network using elliptical curve cryptography. In 2021 IEEE Mysore Sub Section International Conference (MysuruCon). IEEE, 249–254. https://doi.org/10.1109/MysuruCon52639.2021.9641546 PrabakaranD. SheelaK. 2021 A strong authentication for fortifying wireless healthcare sensor network using elliptical curve cryptography In 2021 IEEE Mysore Sub Section International Conference (MysuruCon) IEEE 249 254 https://doi.org/10.1109/MysuruCon52639.2021.9641546 Search in Google Scholar

Batool, A., Byun, Y.-C. (2024). Toward improving breast cancer classification using an adaptive voting ensemble learning algorithm. IEEE Access, 12, 12869–12882. https://doi.org/10.1109/ACCESS.2024.3356602 BatoolA. ByunY.-C. 2024 Toward improving breast cancer classification using an adaptive voting ensemble learning algorithm IEEE Access 12 12869 12882 https://doi.org/10.1109/ACCESS.2024.3356602 Search in Google Scholar

Xie, X., Wu, L., Su, Z., Sun, Z., Cao, X., Hou, Y. (2024). CORONet: A cross-sequence joint representation and hypergraph convolutional network for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. IEEE Journal of Biomedical and Health Informatics, 28 (4), 2103–2114. https://doi.org/10.1109/JBHI.2024.3355111 XieX. WuL. SuZ. SunZ. CaoX. HouY. 2024 CORONet: A cross-sequence joint representation and hypergraph convolutional network for classifying molecular subtypes of breast cancer using incomplete DCE-MRI IEEE Journal of Biomedical and Health Informatics 28 4 2103 2114 https://doi.org/10.1109/JBHI.2024.3355111 Search in Google Scholar

Tiryaki, V. M., Tutkun, N. (2024). Breast cancer mass classification using machine learning, binary-coded genetic algorithms and an ensemble of deep transfer learning. The Computer Journal, 67 (3), 1111–1125. https://doi.org/10.1093/comjnl/bxad046 TiryakiV. M. TutkunN. 2024 Breast cancer mass classification using machine learning, binary-coded genetic algorithms and an ensemble of deep transfer learning The Computer Journal 67 3 1111 1125 https://doi.org/10.1093/comjnl/bxad046 Search in Google Scholar

Li, Z.-Z., Wang, F.-L., Qin, F., Yusoff, Y. B., Zain, A. M. (2024). Feature selection of gene expression data using a modified artificial fish swarm algorithm with population variation. IEEE Access, 12, 72688–72706. https://doi.org/10.1109/ACCESS.2024.3402652 LiZ.-Z. WangF.-L. QinF. YusoffY. B. ZainA. M. 2024 Feature selection of gene expression data using a modified artificial fish swarm algorithm with population variation IEEE Access 12 72688 72706 https://doi.org/10.1109/ACCESS.2024.3402652 Search in Google Scholar

Basaad, A., Basurra, S., Vakaj, E., Aleskandarany, M., Abdelsamea, M. M. (2024). GraphX-Net: A graph neural network-based Shapley values for predicting breast cancer occurrence. IEEE Access, 12, 93993–94007. https://doi.org/10.1109/ACCESS.2024.3424526 BasaadA. BasurraS. VakajE. AleskandaranyM. AbdelsameaM. M. 2024 GraphX-Net: A graph neural network-based Shapley values for predicting breast cancer occurrence IEEE Access 12 93993 94007 https://doi.org/10.1109/ACCESS.2024.3424526 Search in Google Scholar

Supriya, Y., Chengoden, R. (2024). Breast cancer prediction using Shapely and game theory in federated learning environment. IEEE Access, 12, 123018–123037. https://doi.org/10.1109/ACCESS.2024.3424934 SupriyaY. ChengodenR. 2024 Breast cancer prediction using Shapely and game theory in federated learning environment IEEE Access 12 123018 123037 https://doi.org/10.1109/ACCESS.2024.3424934 Search in Google Scholar

Chen, Q., Zhang, J., Meng, R., Zhou, L., Li, Z., Feng, Q. (2024). Modality-specific information disentanglement from multi-parametric MRI for breast tumor segmentation and computer-aided diagnosis. IEEE Transactions on Medical Imaging, 43 (5), 1958–1971. https://doi.org/10.1109/TMI.2024.3352648 ChenQ. ZhangJ. MengR. ZhouL. LiZ. FengQ. 2024 Modality-specific information disentanglement from multi-parametric MRI for breast tumor segmentation and computer-aided diagnosis IEEE Transactions on Medical Imaging 43 5 1958 1971 https://doi.org/10.1109/TMI.2024.3352648 Search in Google Scholar

Furtney, I., Bradley, R., Kabuka, M. R. (2023). Patient graph deep learning to predict breast cancer molecular subtype. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20 (5), 3117–3127. https://doi.org/10.1109/TCBB.2023.3290394 FurtneyI. BradleyR. KabukaM. R. 2023 Patient graph deep learning to predict breast cancer molecular subtype IEEE/ACM Transactions on Computational Biology and Bioinformatics 20 5 3117 3127 https://doi.org/10.1109/TCBB.2023.3290394 Search in Google Scholar

Wang, S., Sun, K., Wang. L., Qu, L., Yan, F., Wang, Q. (2023). Breast tumor segmentation in DCE-MRI with tumor sensitive synthesis. IEEE Transactions on Neural Networks and Learning Systems, 34 (8), 4990–5001. https://doi.org/10.1109/TNNLS.2021.3129781 WangS. SunK. WangL. QuL. YanF. WangQ. 2023 Breast tumor segmentation in DCE-MRI with tumor sensitive synthesis IEEE Transactions on Neural Networks and Learning Systems 34 8 4990 5001 https://doi.org/10.1109/TNNLS.2021.3129781 Search in Google Scholar

Panigrahi, A., Pati, A., Sahu, B., Das, M. N., Nayak, D. S. K., Sahoo, G. (2023). En-MinWhale: An ensemble approach based on MRMR and whale optimization for cancer diagnosis. IEEE Access, 11, 113526–113542. https://doi.org/10.1109/ACCESS.2023.3318261 PanigrahiA. PatiA. SahuB. DasM. N. NayakD. S. K. SahooG. 2023 En-MinWhale: An ensemble approach based on MRMR and whale optimization for cancer diagnosis IEEE Access 11 113526 113542 https://doi.org/10.1109/ACCESS.2023.3318261 Search in Google Scholar

Thakur, T., Batra, I., Malik, A., Ghimire, D., Kim, S.-H., Sanwar Hosen, A. S. M. (2023). RNN-CNN based cancer prediction model for gene expression. IEEE Access, 11, 131024–131044. https://doi.org/10.1109/ACCESS.2023.3332479 ThakurT. BatraI. MalikA. GhimireD. KimS.-H. Sanwar HosenA. S. M. 2023 RNN-CNN based cancer prediction model for gene expression IEEE Access 11 131024 131044 https://doi.org/10.1109/ACCESS.2023.3332479 Search in Google Scholar

Almaslukh, B. (2024). A reliable breast cancer diagnosis approach using an optimized deep learning and conformal prediction. Biomedical Signal Processing and Control, 98, 106743. https://doi.org/10.1016/j.bspc.2024.106743 AlmaslukhB. 2024 A reliable breast cancer diagnosis approach using an optimized deep learning and conformal prediction Biomedical Signal Processing and Control 98 106743 https://doi.org/10.1016/j.bspc.2024.106743 Search in Google Scholar

Crosby, D., Bhatia, S., Brindle, K. M., Coussens, L. M., Dive, C., Emberton, M., Esener, S., Fitzgerald, R. C., Gambhir, S. S., Kuhn, P., Rebbeck, T. R., Balasubramanian, S. (2022). Early detection of cancer. Science, 375 (6586). https://doi.org/10.1126/science.aay9040 CrosbyD. BhatiaS. BrindleK. M. CoussensL. M. DiveC. EmbertonM. EsenerS. FitzgeraldR. C. GambhirS. S. KuhnP. RebbeckT. R. BalasubramanianS. 2022 Early detection of cancer Science 375 6586 https://doi.org/10.1126/science.aay9040 Search in Google Scholar

Rashid, T. A., Majidpour, J., Thinakaran, R., Batumalay, M., Arrova Dewi, D., Hassan, B. A. (2024). NSGA-II-DL: Metaheuristic optimal feature selection with deep learning framework for HER2 classification in breast cancer. IEEE Access, 12, 38885–38898. https://doi.org/10.1109/ACCESS.2024.3374890 RashidT. A. MajidpourJ. ThinakaranR. BatumalayM. Arrova DewiD. HassanB. A. 2024 NSGA-II-DL: Metaheuristic optimal feature selection with deep learning framework for HER2 classification in breast cancer IEEE Access 12 38885 38898 https://doi.org/10.1109/ACCESS.2024.3374890 Search in Google Scholar

Mirimoghaddam, M. M., Majidpour, J., Pashaei, F., Arabalibeik, H., Samizadeh, E., Roshan, N. M., Rashid, T. A. (2024). HER2GAN: Overcome the scarcity of HER2 breast cancer dataset based on transfer learning and GAN model. Clinical Breast Cancer, 24 (1), 53–64. https://doi.org/10.1016/j.clbc.2023.09.014 MirimoghaddamM. M. MajidpourJ. PashaeiF. ArabalibeikH. SamizadehE. RoshanN. M. RashidT. A. 2024 HER2GAN: Overcome the scarcity of HER2 breast cancer dataset based on transfer learning and GAN model Clinical Breast Cancer 24 1 53 64 https://doi.org/10.1016/j.clbc.2023.09.014 Search in Google Scholar

Issa, A. S., Ali, Y. H., Rashid, T. A. (2023). Review on hybrid swarm algorithms for feature selection. Iraqi Journal of Science, 64 (10), 5331–5344. https://doi.org/10.24996/ijs.2023.64.10.38 IssaA. S. AliY. H. RashidT. A. 2023 Review on hybrid swarm algorithms for feature selection Iraqi Journal of Science 64 10 5331 5344 https://doi.org/10.24996/ijs.2023.64.10.38 Search in Google Scholar

Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28, 104863. https://doi.org/10.1016/j.dib.2019.104863 Al-DhabyaniW. GomaaM. KhaledH. FahmyA. 2020 Dataset of breast ultrasound images Data in Brief 28 104863 https://doi.org/10.1016/j.dib.2019.104863 Search in Google Scholar

Long, J., Zheng, Z., Wang, J., Ng, C. K., Liu, C., Ji, W. (2024). Decision tree based automated detection of breast cancer. In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 549–554. https://doi.org/10.1109/CIS-RAM61939.2024.10673070 LongJ. ZhengZ. WangJ. NgC. K. LiuC. JiW. 2024 Decision tree based automated detection of breast cancer In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM) IEEE 549 554 https://doi.org/10.1109/CIS-RAM61939.2024.10673070 Search in Google Scholar

Rahman, S., Siregar, D., Syah, R. B. Y., Setiawan, H., Maulana, A. E., Hamsiah. (2023). The effective breast cancer classification with the random forest algorithm. In 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM). IEEE. https://doi.org/10.1109/ICoSNIKOM60230.2023.10364529 RahmanS. SiregarD. SyahR. B. Y. SetiawanH. MaulanaA. E. Hamsiah 2023 The effective breast cancer classification with the random forest algorithm In 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM) IEEE https://doi.org/10.1109/ICoSNIKOM60230.2023.10364529 Search in Google Scholar

Khater, T., Hussain, A., Bendardaf, R., Talaat, I. M., Tawfik. H., Ansari, S., Mahmoud, S. (2025). An explainable artificial intelligence model for the classification of breast cancer. In IEEE Access, 13, 5618–5633. https://doi.org/10.1109/ACCESS.2023.3308446 KhaterT. HussainA. BendardafR. TalaatI. M. TawfikH. AnsariS. MahmoudS. 2025 An explainable artificial intelligence model for the classification of breast cancer In IEEE Access 13 5618 5633 https://doi.org/10.1109/ACCESS.2023.3308446 Search in Google Scholar

Maouche, I., Terrissa, L. S., Benmohammed K., Zerhouni, N. (2023). An explainable AI approach for breast cancer metastasis prediction based on clinicopathological data. In IEEE Transactions on Biomedical Engineering, 70 (12), 3321–3329. https://doi.org/10.1109/TBME.2023.3282840 MaoucheI. TerrissaL. S. BenmohammedK. ZerhouniN. 2023 An explainable AI approach for breast cancer metastasis prediction based on clinicopathological data In IEEE Transactions on Biomedical Engineering 70 12 3321 3329 https://doi.org/10.1109/TBME.2023.3282840 Search in Google Scholar

Shukla, V., Kaarthika, Mathur, A., Narayan P., Kishor, K. (2025). A multi-modal approach for the molecular subtype classification of breast cancer by using Vision Transformer and novel SVM polyvariant kernel. In IEEE Access, 13, 97545–97558. https://doi.org/10.1109/ACCESS.2025.3575126 ShuklaV. Kaarthika MathurA. NarayanP. KishorK. 2025 A multi-modal approach for the molecular subtype classification of breast cancer by using Vision Transformer and novel SVM polyvariant kernel In IEEE Access 13 97545 97558 https://doi.org/10.1109/ACCESS.2025.3575126 Search in Google Scholar

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