{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:09:58Z","timestamp":1772928598566,"version":"3.50.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"DOI":"10.1007\/s13721-026-00738-y","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:34:46Z","timestamp":1772858086000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced breast cancer detection via Shapley additive explanations and attention-based bidirectional LSTM with ensemble learning models"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0804-0707","authenticated-orcid":false,"given":"Yakub Kayode","family":"Saheed","sequence":"first","affiliation":[]},{"given":"Mokhairi","family":"Makhtar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,7]]},"reference":[{"key":"738_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.11.004","author":"M Abdar","year":"2018","unstructured":"Abdar M et al (2018) A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognit Lett. https:\/\/doi.org\/10.1016\/j.patrec.2018.11.004","journal-title":"Pattern Recognit Lett"},{"key":"738_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/spy2.70044","author":"OH Abdulganiyu","year":"2025","unstructured":"Abdulganiyu OH, Tchakoucht TA, Saheed YK, El Mouhtadi M, Alaoui AEH (2025) Modified variational autoencoder and attention mechanism-based long short\u2010term memory for detecting intrusions in imbalanced network traffic. SECURITY AND PRIVACY. https:\/\/doi.org\/10.1002\/spy2.70044","journal-title":"SECURITY AND PRIVACY"},{"key":"738_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2025.200519","volume":"26","author":"OH Abdulganiyu","year":"2025","unstructured":"Abdulganiyu OH, Ait Tchakoucht T, Alaoui AEH, Saheed YK (2025) Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting. Intelligent Systems with Applications 26:200519. https:\/\/doi.org\/10.1016\/j.iswa.2025.200519","journal-title":"Intelligent Systems with Applications"},{"key":"738_CR4","doi-asserted-by":"publisher","unstructured":"Agarap, A. F. M. (2018, February). On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset. In Proceedings of the 2nd international conference on machine learning and soft computing (pp. 5-9). https:\/\/doi.org\/10.1145\/3184066.3184080","DOI":"10.1145\/3184066.3184080"},{"key":"738_CR5","doi-asserted-by":"crossref","unstructured":"Ak, M. F. (2020, April). A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications. In Healthcare (Vol. 8, No. 2, p. 111). MDPI https:\/\/www.mdpi.com\/2227-9032\/8\/2\/111","DOI":"10.3390\/healthcare8020111"},{"issue":"1","key":"738_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.47363\/jonrr\/2021(2)118","volume":"2","author":"AJ Alkhatib","year":"2020","unstructured":"Alkhatib AJ, Alkhatib SM (2020) Prediction of breast cancer risk factors using neural network analytics: an empirical study. J. Oncol. Res. Rev. Reports 2(1):1\u20135. https:\/\/doi.org\/10.47363\/jonrr\/2021(2)118","journal-title":"J. Oncol. Res. Rev. Reports"},{"issue":"1","key":"738_CR7","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/1116\/1\/012187","volume":"1116","author":"PR Anisha","year":"2021","unstructured":"Anisha PR, Reddy CKK, Apoorva K, Mangipudi CM (2021) Early diagnosis of breast cancer prediction using random forest classifier. IOP Conf. Ser. Mater. Sci. Eng. 1116(1):012187. https:\/\/doi.org\/10.1088\/1757-899x\/1116\/1\/012187","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"738_CR8","doi-asserted-by":"publisher","first-page":"37","DOI":"10.5121\/csit.2011.1205","volume":"2","author":"S Aruna","year":"2011","unstructured":"Aruna S, Rajagopalan SP, Nandakishore LV (2011) Knowledge based analysis of various statistical tools in detecting breast cancer. Comput Sci Inf Technol 2:37\u201345. https:\/\/doi.org\/10.5121\/csit.2011.1205","journal-title":"Comput Sci Inf Technol"},{"key":"738_CR9","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.procs.2016.04.224","volume":"83","author":"H Asri","year":"2016","unstructured":"Asri H, Mousannif H, Al Moatassime H, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 83:1064\u20131069. https:\/\/doi.org\/10.1016\/j.procs.2016.04.224","journal-title":"Procedia Comput Sci"},{"issue":"3","key":"738_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.18196\/jrc.2363","volume":"2","author":"TA Assegie","year":"2021","unstructured":"Assegie TA (2021) An optimized K-nearest neighbor based breast cancer detection. J Robot Control 2(3):115\u2013118. https:\/\/doi.org\/10.18196\/jrc.2363","journal-title":"J Robot Control"},{"key":"738_CR11","doi-asserted-by":"publisher","unstructured":"Saheed YK, Misra S, Chockalingam S Autoencoder via DCNN and LSTM Models for Intrusion Detection in Industrial Control Systems of Critical Infrastructures, (2023) IEEE\/ACM 4th Int. Work. Eng. Cybersecurity Crit. Syst. (EnCyCriS), Melbourne, Aust., pp. 9\u201316, 2023. https:\/\/doi.org\/10.1109\/EnCyCriS59249.2023.00006","DOI":"10.1109\/EnCyCriS59249.2023.00006"},{"key":"738_CR12","doi-asserted-by":"publisher","unstructured":"Babu G. A., Bhukya S. N., Kumar R. S. (2013) Feed forward network with back propagation algorithm for detection of breast cancer. Proc 8th Int Conf Comput Sci Educ ICCSE 2013 no Iccse:181\u2013185. https:\/\/doi.org\/10.1109\/ICCSE.2013.6553907","DOI":"10.1109\/ICCSE.2013.6553907"},{"issue":"10","key":"738_CR13","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.22034\/APJCP.2018.19.10.2917","volume":"19","author":"A Bazila Banu","year":"2018","unstructured":"Bazila Banu A, Thirumalaikolundusubramanian P (2018) Comparison of bayes classifiers for breast cancer classification. Asian Pac J Cancer Prev 19(10):2917\u20132920. https:\/\/doi.org\/10.22034\/APJCP.2018.19.10.2917","journal-title":"Asian Pac J Cancer Prev"},{"key":"738_CR14","doi-asserted-by":"publisher","first-page":"18","DOI":"10.25080\/majora-92bf1922-003","volume":"no Scipy","author":"J Bergstra","year":"2010","unstructured":"Bergstra J et al (2010) Theano: A CPU and GPU math compiler in python. Proc 9th Python Sci Conf no Scipy:18\u201324. https:\/\/doi.org\/10.25080\/majora-92bf1922-003","journal-title":"Proc 9th Python Sci Conf"},{"key":"738_CR15","doi-asserted-by":"publisher","unstructured":"Bharati S, Rahman MA, Podder P (2019) Breast cancer prediction applying different classification algorithm with comparative analysis using WEKA. 4th Int Conf Electr Eng Inf Commun Technol iCEEiCT 2018 581\u2013584. https:\/\/doi.org\/10.1109\/CEEICT.2018.8628084","DOI":"10.1109\/CEEICT.2018.8628084"},{"key":"738_CR16","doi-asserted-by":"publisher","unstructured":"Barbieri RL Breast, Yen Jaffe\u2019s Reprod. Endocrinol. Physiol. Pathophysiol. Clin. Manag. Eighth Ed., vol. 419, pp. 248\u2013255.e3, 2019. https:\/\/doi.org\/10.1016\/B978-0-323-47912-7.00010-X","DOI":"10.1016\/B978-0-323-47912-7.00010-X"},{"key":"738_CR17","doi-asserted-by":"publisher","unstructured":"Hamad Y, Simonov K, Naeem MB (2019) Breast cancer detection and classification using artificial neural networks. Proc - 2018 1st Annu Int Conf Inf Sci AiCIS 2018 51\u201357. https:\/\/doi.org\/10.1109\/AiCIS.2018.00022","DOI":"10.1109\/AiCIS.2018.00022"},{"issue":"3","key":"738_CR18","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"8","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 8(3):123\u2013140. https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Mach Learn"},{"key":"738_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"LEO Breiman","year":"2001","unstructured":"Breiman LEO (2001) Random forests. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"issue":"3","key":"738_CR20","first-page":"10","volume":"1","author":"V Chaurasia","year":"2014","unstructured":"Chaurasia V, Pal S (2014) Data mining techniques: to predict and resolve breast cancer survivability. Int J Comput Sci Mob Comput IJCSMC 1(3):10\u201322","journal-title":"Int J Comput Sci Mob Comput IJCSMC"},{"issue":"7","key":"738_CR21","doi-asserted-by":"publisher","first-page":"9014","DOI":"10.1016\/j.eswa.2011.01.120","volume":"38","author":"HL Chen","year":"2011","unstructured":"Chen HL, Yang B, Liu J, Liu DY (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014\u20139022. https:\/\/doi.org\/10.1016\/j.eswa.2011.01.120","journal-title":"Expert Syst Appl"},{"key":"738_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj-cs.2784","volume":"11","author":"M Chetry","year":"2025","unstructured":"Chetry M et al (2025) Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes. PeerJ Comput Sci 11:1\u201328. https:\/\/doi.org\/10.7717\/peerj-cs.2784","journal-title":"PeerJ Comput Sci"},{"key":"738_CR23","doi-asserted-by":"publisher","unstructured":"Randhawa K, Loo CK, Member S, Seera M, Lim CP, Nandi AK (2018) Credit card fraud detection using adaboost and majority voting. XX:1\u20138. https:\/\/doi.org\/10.1109\/ACCESS.2018.2806420","DOI":"10.1109\/ACCESS.2018.2806420"},{"key":"738_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2004.07.002","author":"D Delen","year":"2005","unstructured":"Delen D, Walker G, Kadam A (2005) Predicting breast cancer survivability\u202f: a comparison of three data mining methods. Artif Intell Med. https:\/\/doi.org\/10.1016\/j.artmed.2004.07.002","journal-title":"Artif Intell Med"},{"issue":"February","key":"738_CR25","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng H, Runger G, Tuv E, Vladimir M (2013) A time series forest for classification and feature extraction. Inf Sci (Ny) 239(February):142\u2013153. https:\/\/doi.org\/10.1016\/j.ins.2013.02.030","journal-title":"Inf Sci (Ny)"},{"key":"738_CR26","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/4253641","author":"H Dhahri","year":"2019","unstructured":"Dhahri H, Al Maghayreh E, Mahmood A, Elkilani W, Faisal Nagi M (2019) Automated breast cancer diagnosis based on machine learning algorithms. J Healthc Eng. https:\/\/doi.org\/10.1155\/2019\/4253641","journal-title":"J Healthc Eng"},{"key":"738_CR27","doi-asserted-by":"crossref","unstructured":"Du, Q., Zhao, L., Xu, J., Han, Y., & Zhang, S. (2021, August). Log-based anomaly detection with multi-head scaled dot-product attention mechanism. In International conference on database and expert systems applications (pp. 335-347). Cham: Springer International Publishing. https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-86472-9_31","DOI":"10.1007\/978-3-030-86472-9_31"},{"issue":"2","key":"738_CR28","first-page":"92","volume":"36","author":"D Dumitru","year":"2009","unstructured":"Dumitru D (2009) Prediction of recurrent events in breast cancer using the Naive Bayesian classification. Ann Univ Craiova Math Comp Sci Ser 36(2):92\u201396","journal-title":"Ann Univ Craiova Math Comp Sci Ser"},{"key":"738_CR29","doi-asserted-by":"publisher","unstructured":"Usman ASNTM, Saheed YK Ens5B-UNet for Improved Microaneurysms Segmentation in Retinal Images, in (2024) International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), Omu-Aran, Nigeria, 2024, 2024, pp. 1\u20136. https:\/\/doi.org\/10.1109\/SEB4SDG60871.2024.10629958","DOI":"10.1109\/SEB4SDG60871.2024.10629958"},{"issue":"5","key":"738_CR30","doi-asserted-by":"publisher","first-page":"E359","DOI":"10.1002\/ijc.29210","volume":"136","author":"J Ferlay","year":"2015","unstructured":"Ferlay J et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136(5):E359\u2013E386. https:\/\/doi.org\/10.1002\/ijc.29210","journal-title":"Int J Cancer"},{"key":"738_CR31","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/4563524","author":"ZG Gao","year":"2016","unstructured":"Gao ZG, Wang L, Xia SX, You ZH, Yan X, Zhou Y (2016) Ens-PPI: a novel ensemble classifier for predicting the interactions of proteins using autocovariance transformation from PSSM. Biomed Res Int. https:\/\/doi.org\/10.1155\/2016\/4563524","journal-title":"Biomed Res Int"},{"key":"738_CR32","doi-asserted-by":"publisher","unstructured":"Usman TM, Ajibesin AA, Saheed YK, Nsang AS, GAPS-U-NET: Gating Attention And Pixel Shuffling U-Net For Optic Disc Segmentation In Retinal Images, in (2023) 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), Abuja, Nigeria, 2023, 2023, pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICMEAS58693.2023.10429873","DOI":"10.1109\/ICMEAS58693.2023.10429873"},{"issue":"6","key":"738_CR33","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","volume":"57","author":"Y Hua","year":"2019","unstructured":"Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H (2019) Deep learning with long short-term memory for time series prediction. IEEE Commun Mag 57(6):114\u2013119. https:\/\/doi.org\/10.1109\/MCOM.2019.1800155","journal-title":"IEEE Commun Mag"},{"key":"738_CR34","doi-asserted-by":"crossref","unstructured":"Gupta, S., Arango-Argoty, G., Zhang, L., Pruden, A., & Vikesland, P. (2019). Identification of discriminatory antibiotic resistance genes among environmental resistomes using extremely randomized tree algorithm. Microbiome, 7(1), 123. https:\/\/link.springer.com\/article\/10.1186\/s40168-019-0735-1","DOI":"10.1186\/s40168-019-0735-1"},{"key":"738_CR35","doi-asserted-by":"publisher","unstructured":"Kaushik D, Kaur K (2016) Application of data mining for high accuracy prediction of breast tissue biopsy results. 2016 3rd Int Conf Digit Inf Process Data Min Wirel Commun DIPDMWC 2016 40\u201345. https:\/\/doi.org\/10.1109\/DIPDMWC.2016.7529361","DOI":"10.1109\/DIPDMWC.2016.7529361"},{"issue":"1","key":"738_CR36","first-page":"149","volume":"26","author":"MK Kele\u015f","year":"2019","unstructured":"Kele\u015f MK (2019) Breast cancer prediction and detection using data mining classification algorithms: a comparative study. Teh Vjesn 26(1):149\u2013155","journal-title":"Teh Vjesn"},{"key":"738_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3152856","volume":"71","author":"CF Lui","year":"2022","unstructured":"Lui CF, Liu Y, Xie M (2022) A supervised bidirectional long short-term memory network for data-driven dynamic soft sensor modeling. IEEE Trans Instrum Meas 71:1\u201313. https:\/\/doi.org\/10.1109\/TIM.2022.3152856","journal-title":"IEEE Trans Instrum Meas"},{"issue":"4","key":"738_CR38","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.jacr.2018.09.041","volume":"16","author":"N Mao","year":"2019","unstructured":"Mao N et al (2019) Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol 16(4):485\u2013491. https:\/\/doi.org\/10.1016\/j.jacr.2018.09.041","journal-title":"J Am Coll Radiol"},{"key":"738_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2019.03.001","volume":"810","author":"P Mehta","year":"2019","unstructured":"Mehta P et al (2019) A high-bias, low-variance introduction to machine learning for physicists. Phys Rep 810:1\u2013124. https:\/\/doi.org\/10.1016\/j.physrep.2019.03.001","journal-title":"Phys Rep"},{"key":"738_CR40","doi-asserted-by":"publisher","unstructured":"Adeyiola AQ, Saheed YK, Misra S, Chockalingam S Metaheuristic Firefly and C5. 0 Algorithms Based Intrusion Detection for Critical Infrastructures, in (2023) 3rd International Conference on Applied Artificial Intelligence (ICAPAI), 2023, pp. 1\u20137. https:\/\/doi.org\/10.1109\/ICAPAI58366.2023.10193917","DOI":"10.1109\/ICAPAI58366.2023.10193917"},{"key":"738_CR41","doi-asserted-by":"crossref","unstructured":"Ani, R., Jose, J., Wilson, M., & Deepa, O. S. (2017). Modified rotation forest ensemble classifier for medical diagnosis in decision support systems. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2016, Volume 2 (pp. 137-146). Singapore: Springer Singapore https:\/\/link.springer.com\/chapter\/10.1007\/978-981-10-6875-1_14","DOI":"10.1007\/978-981-10-6875-1_14"},{"key":"738_CR42","doi-asserted-by":"crossref","unstructured":"Mohammed, S. A., Darrab, S., Noaman, S. A., & Saake, G. (2020, July). Analysis of breast cancer detection using different machine learning techniques. In International conference on data mining and big data (pp. 108-117). Singapore: Springer Singapore. https:\/\/link.springer.com\/chapter\/10.1007\/978-981-15-7205-0_10","DOI":"10.1007\/978-981-15-7205-0_10"},{"issue":"47","key":"738_CR43","doi-asserted-by":"publisher","first-page":"26652","DOI":"10.1007\/s11356-025-37137-1","volume":"32","author":"M Mustapha","year":"2025","unstructured":"Mustapha M, Zineddine M, Abdulganiyu OH, Saheed YK, Alaoui AEH (2025) Towards efficient artificial intelligence techniques for the assessment of irrigation water quality: a systematic literature review. Environ Sci Pollut Res 32(47):26652\u201326701. https:\/\/doi.org\/10.1007\/s11356-025-37137-1","journal-title":"Environ Sci Pollut Res"},{"issue":"May","key":"738_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59720\/20-131","volume":"3","author":"S Nag","year":"2021","unstructured":"Nag S, Nag J (2021) A comparative analysis of machine learning approaches for prediction of breast cancer. J Emerg Investig 3(May):1\u20139. https:\/\/doi.org\/10.59720\/20-131","journal-title":"J Emerg Investig"},{"issue":"9","key":"738_CR45","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s10552-011-9805-9","volume":"22","author":"SJ Nechuta","year":"2011","unstructured":"Nechuta SJ et al (2011) The after breast cancer pooling project: rationale, methodology, and breast cancer survivor characteristics. Cancer Causes Control 22(9):1319\u20131331. https:\/\/doi.org\/10.1007\/s10552-011-9805-9","journal-title":"Cancer Causes Control"},{"issue":"6","key":"738_CR46","first-page":"21","volume":"28","author":"R Nithya","year":"2011","unstructured":"R. Nithya, B. Santhi . Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer. International Journal of Computer Applications. 28, 6 (August 2011), 21-25. DOI=10.5120\/3391-4707","journal-title":"Int J Comput Appl"},{"key":"738_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.103071","volume":"24","author":"OO Oladimeji","year":"2024","unstructured":"Oladimeji OO, Ayaz H, McLoughlin I, Unnikrishnan S (2024) Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis. Results Eng 24:103071. https:\/\/doi.org\/10.1016\/j.rineng.2024.103071","journal-title":"Results Eng"},{"key":"738_CR48","doi-asserted-by":"publisher","first-page":"39165","DOI":"10.1109\/ACCESS.2020.2976149","volume":"8","author":"AH Osman","year":"2020","unstructured":"Osman AH, Aljahdali HMA (2020) An effective of ensemble boosting learning method for breast cancer virtual screening using neural network model. IEEE Access 8:39165\u201339174. https:\/\/doi.org\/10.1109\/ACCESS.2020.2976149","journal-title":"IEEE Access"},{"issue":"4","key":"738_CR49","doi-asserted-by":"publisher","first-page":"142","DOI":"10.19072\/ijet.280563","volume":"2","author":"D Oyewola","year":"2017","unstructured":"Oyewola, D., Hakimi, D., Adeboye, K., & Shehu, M. D. (2016). Using five machine learning for breast cancer biopsy predictions based on mammographic diagnosis. International Journal of Engineering Technologies IJET, 2(4), 142-145.","journal-title":"International Journal of Engineering Technologies IJET"},{"issue":"1","key":"738_CR50","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1093\/epirev\/mxx003","volume":"39","author":"M Pi\u00f1eros","year":"2017","unstructured":"Pi\u00f1eros M, Znaor A, Mery L, Bray F (2017) A global cancer surveillance framework within noncommunicable disease surveillance: making the case for population-based cancer registries. Epidemiol Rev 39(1):161\u2013169. https:\/\/doi.org\/10.1093\/epirev\/mxx003","journal-title":"Epidemiol Rev"},{"issue":"September 2020","key":"738_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107284","volume":"205","author":"U Saeed","year":"2021","unstructured":"Saeed U, Jan SU, Lee Y, Koo I (2021) Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliab Eng Syst Saf 205(September 2020):107284. https:\/\/doi.org\/10.1016\/j.ress.2020.107284","journal-title":"Reliab Eng Syst Saf"},{"issue":"1","key":"738_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-80535-7","volume":"15","author":"S Saharan","year":"2025","unstructured":"Saharan S, Wani NA, Chatterji S, Kumar N, Almuhaideb AM (2025) A deep learning and explainable artificial intelligence based scheme for breast cancer detection. Sci Rep 15(1):1\u201316. https:\/\/doi.org\/10.1038\/s41598-024-80535-7","journal-title":"Sci Rep"},{"issue":"6","key":"738_CR53","doi-asserted-by":"publisher","first-page":"3575","DOI":"10.1109\/TCBB.2023.3305429","volume":"20","author":"YK Saheed","year":"2023","unstructured":"Saheed YK, Balogun BF, Odunayo BJ, Abdulsalam M (2023) Microarray gene expression data classification via Wilcoxon sign rank sum and novel grey wolf optimized ensemble learning models. IEEE\/ACM Transactions on Computational Biology and Bioinformatics 20(6):3575\u20133587. https:\/\/doi.org\/10.1109\/TCBB.2023.3305429","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"738_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcip.2024.100674","volume":"45","author":"YK Saheed","year":"2024","unstructured":"Saheed YK, Abdulganiyu OH, Majikumna KU, Mustapha M, Workneh AD (2024) ResNet50-1D-CNN: a new lightweight resnet50-one-dimensional convolution neural network transfer learning-based approach for improved intrusion detection in cyber-physical systems. Int J Crit Infrastruct Prot 45:100674. https:\/\/doi.org\/10.1016\/j.ijcip.2024.100674","journal-title":"Int J Crit Infrastruct Prot"},{"key":"738_CR55","doi-asserted-by":"crossref","unstructured":"Saheed, Y. K. (2022). Data analytics for intrusion detection system based on recurrent neural network and supervised machine learning methods. In Recurrent Neural Networks (pp. 167-179). CRC Press.","DOI":"10.1201\/9781003307822-12"},{"issue":"February","key":"738_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106319","volume":"94","author":"YK Saheed","year":"2024","unstructured":"Saheed YK, Salau-Ibrahim TT, Abdulsalam M, Adeniji IA, Balogun BF (2024) Modified bi-directional long short-term memory and hyperparameter tuning of supervised machine learning models for cardiovascular heart disease prediction in mobile cloud environment. Biomed Signal Process Control 94(February):106319. https:\/\/doi.org\/10.1016\/j.bspc.2024.106319","journal-title":"Biomed Signal Process Control"},{"issue":"3","key":"738_CR57","first-page":"338","volume":"12","author":"YK Saheed","year":"2020","unstructured":"Saheed YK, Hamza-Usman FE (2020) Feature selection with IG-R for improving performance of intrusion detection system. Int J Commun Networks Inf Secur 12(3):338\u2013344","journal-title":"Int J Commun Networks Inf Secur"},{"key":"738_CR58","doi-asserted-by":"publisher","unstructured":"Saheed YK, Abdulganiyu OH, Abdulsalam M, Mustapha M, Olivier MM, Majikumna KU A Hybrid Ant Colony Optimization for Parkinson\u2019s Disease Classification Based on Synthetic Minority Oversampling and Adaptive Synthetic Techniques, (2024) 5th Int. Conf. Data Anal. Bus. Ind. ICDABI 2024, pp. 16\u201323, 2024. https:\/\/doi.org\/10.1109\/ICDABI63787.2024.10800028","DOI":"10.1109\/ICDABI63787.2024.10800028"},{"key":"738_CR59","doi-asserted-by":"publisher","unstructured":"Saheed YK, Abdulsalam SO, Ibrahim MB, Baba UA Towards a New Hybrid Synthetic Minority Oversampling Technique for Imbalanced Problem in Software Defect Prediction, (2024) 5th Int. Conf. Data Anal. Bus. Ind. ICDABI 2024, pp. 224\u2013231, 2024. https:\/\/doi.org\/10.1109\/ICDABI63787.2024.10800331","DOI":"10.1109\/ICDABI63787.2024.10800331"},{"issue":"1","key":"738_CR60","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1536867X20909688","volume":"20","author":"M Schonlau","year":"2020","unstructured":"Schonlau M, Zou RY (2020) The random forest algorithm for statistical learning. Stata J 20(1):3\u201329. https:\/\/doi.org\/10.1177\/1536867X20909688","journal-title":"Stata J"},{"issue":"3","key":"738_CR61","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s10549-014-2980-0","volume":"145","author":"P Sharma","year":"2014","unstructured":"Sharma P et al (2014) Germline BRCA mutation evaluation in a prospective triple-negative breast cancer registry: implications for hereditary breast and\/or ovarian cancer syndrome testing. Breast Cancer Res Treat 145(3):707\u2013714. https:\/\/doi.org\/10.1007\/s10549-014-2980-0","journal-title":"Breast Cancer Res Treat"},{"key":"738_CR62","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.swevo.2013.04.004","volume":"12","author":"P Shunmugapriya","year":"2013","unstructured":"Shunmugapriya P, Kanmani S (2013) Optimization of stacking ensemble configurations through artificial bee colony algorithm. Swarm Evol Comput 12:24\u201332. https:\/\/doi.org\/10.1016\/j.swevo.2013.04.004","journal-title":"Swarm Evol Comput"},{"issue":"10","key":"738_CR63","doi-asserted-by":"publisher","first-page":"16","DOI":"10.5120\/17219-7456","volume":"98","author":"R Sumbaly","year":"2014","unstructured":"Sumbaly R, Vishnusri N, Jeyalatha S (2014) Diagnosis of breast cancer using decision tree data mining technique. Int J Comput Appl 98(10):16\u201324. https:\/\/doi.org\/10.5120\/17219-7456","journal-title":"Int J Comput Appl"},{"issue":"8","key":"738_CR64","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785","volume":"42","author":"C Tianqi","year":"2016","unstructured":"Tianqi C, Guestrin C (2016) XGBoost: a scalable tree boosting system. KDD 42(8):665. https:\/\/doi.org\/10.1145\/2939672.2939785","journal-title":"KDD"},{"issue":"4","key":"738_CR65","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.icte.2020.04.009","volume":"6","author":"AR Vaka","year":"2020","unstructured":"Vaka AR, Soni B (2020) Breast cancer detection by leveraging machine learning. ICT Express 6(4):320\u2013324. https:\/\/doi.org\/10.1016\/j.icte.2020.04.009","journal-title":"ICT Express"},{"key":"738_CR66","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/9360941","author":"G Valvano","year":"2019","unstructured":"Valvano G et al (2019) Convolutional neural networks for the segmentation of microcalcification in mammography imaging. J Healthc Eng. https:\/\/doi.org\/10.1155\/2019\/9360941","journal-title":"J Healthc Eng"},{"issue":"8","key":"738_CR67","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Van Houdt","year":"2020","unstructured":"Van Houdt G, Mosquera C, N\u00e1poles G (2020) A review on the long short-term memory model. Artif Intell Rev 53(8):5929\u20135955. https:\/\/doi.org\/10.1007\/s10462-020-09838-1","journal-title":"Artif Intell Rev"},{"key":"738_CR68","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4015613","author":"H Wang","year":"2018","unstructured":"Wang H et al (2018) Breast mass detection in digital mammogram based on gestalt psychology. J Healthc Eng. https:\/\/doi.org\/10.1155\/2018\/4015613","journal-title":"J Healthc Eng"},{"key":"738_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.12.001","author":"H Wang","year":"2017","unstructured":"Wang H, Zheng B, Yoon SW, Ko HS (2017) PT US CR. Eur J Oper Res. https:\/\/doi.org\/10.1016\/j.ejor.2017.12.001","journal-title":"Eur J Oper Res"},{"issue":"no. May 2015","key":"738_CR70","first-page":"818","volume":"2015","author":"H Wang","year":"2015","unstructured":"Wang H (2015) S. W. <>Yoon Breast cancer prediction using data mining method. IIE Annu Conf Expo 2015 no. May 2015 818\u2013828","journal-title":"IIE Annu Conf Expo"},{"key":"738_CR71","doi-asserted-by":"publisher","DOI":"10.24432\/C5HP4Z","author":"W Wolberg","year":"1990","unstructured":"Wolberg W (1990) Breast cancer Wisconsin (Original) [Dataset]. UCI Mach Learn Repos. https:\/\/doi.org\/10.24432\/C5HP4Z","journal-title":"UCI Mach Learn Repos"},{"key":"738_CR72","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","volume":"78","author":"Y Xia","year":"2017","unstructured":"Xia Y, Liu C, Li YY, Liu N (2017) A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225\u2013241. https:\/\/doi.org\/10.1016\/j.eswa.2017.02.017","journal-title":"Expert Syst Appl"},{"key":"738_CR73","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5036710","author":"J Yan","year":"2018","unstructured":"Yan J, Han S (2018) Classifying imbalanced data sets by a novel re-sample and cost-sensitive stacked generalization method. Math Probl Eng. https:\/\/doi.org\/10.1155\/2018\/5036710","journal-title":"Math Probl Eng"},{"key":"738_CR74","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1007\/s11517-020-02187-9","volume":"58","author":"E Yavuz","year":"2020","unstructured":"Yavuz E, Eyupoglu C (2020) An effective approach for breast cancer diagnosis based on routine blood analysis features. Med Biol Eng Comput 58:1583\u20131601. https:\/\/doi.org\/10.1007\/s11517-020-02187-9","journal-title":"Med Biol Eng Comput"},{"key":"738_CR75","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-019-09999-3","author":"C Zhang","year":"2019","unstructured":"Zhang C, Zhang Y, Shi X, Almpanidis G, Fan G, Shen X (2019) On incremental learning for gradient boosting decision trees. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-019-09999-3","journal-title":"Neural Process Lett"},{"key":"738_CR76","doi-asserted-by":"publisher","first-page":"3986","DOI":"10.1177\/1077546314568172","volume":"22","author":"J Zhou","year":"2016","unstructured":"Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. JVC\/Journal Vib Control 22:3986\u20133997. https:\/\/doi.org\/10.1177\/1077546314568172","journal-title":"JVC\/Journal Vib Control"},{"key":"738_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrmms.2021.104856","volume":"145","author":"J Zhou","year":"2021","unstructured":"Zhou J, Qiu Y, Khandelwal M, Zhu S, Zhang X (2021) Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min Sci 145:104856. https:\/\/doi.org\/10.1016\/j.ijrmms.2021.104856","journal-title":"Int J Rock Mech Min Sci"},{"key":"738_CR78","doi-asserted-by":"publisher","DOI":"10.14738\/tnc.32.662","author":"PA Idowu","year":"2015","unstructured":"Idowu PA, Williams KO, Balogun JA, Oluwaranti AI (2015) no March. https:\/\/doi.org\/10.14738\/tnc.32.662. Breast Cancer Risk Prediction Using Data Mining Classification Techniques"},{"key":"738_CR79","doi-asserted-by":"publisher","unstructured":"Sagi O, Rokach L (2017) Ensemble learning: A survey, no. December pp. 1\u201318, 2018. https:\/\/doi.org\/10.1002\/widm.1249","DOI":"10.1002\/widm.1249"},{"key":"738_CR80","doi-asserted-by":"crossref","unstructured":"Saheed YK (2023) Effective dimensionality reduction model with machine learning classification for microarray gene expression data, in Data Science for Genomics, pp. 153\u2013164, [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/B9780323983525000069","DOI":"10.1016\/B978-0-323-98352-5.00006-9"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-026-00738-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-026-00738-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-026-00738-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:34:52Z","timestamp":1772858092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-026-00738-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,7]]},"references-count":80,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["738"],"URL":"https:\/\/doi.org\/10.1007\/s13721-026-00738-y","relation":{"references":[{"id-type":"doi","id":"10.14738\/tnc.32.662","asserted-by":"subject"}]},"ISSN":["2192-6670"],"issn-type":[{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,7]]},"assertion":[{"value":"11 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethical approval"}}],"article-number":"82"}}