{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:28:22Z","timestamp":1774628902709,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03174-6","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T10:59:17Z","timestamp":1756724357000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Developing multimodal cervical cancer risk assessment and prediction model based on LMIC hospital patient card sheets and histopathological images"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2180-7725","authenticated-orcid":false,"given":"Kelebet Chane","family":"Jemane","sequence":"first","affiliation":[]},{"given":"Muktar Bedaso","family":"Kuyu","sequence":"additional","affiliation":[]},{"given":"Geletaw Sahle","family":"Tegenaw","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"3174_CR1","unstructured":"Noncommunicable diseases, Accessed. Dec. 07, 2024. [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/noncommunicable-diseases"},{"key":"3174_CR2","unstructured":"Cervical cancer., Accessed. Dec. 07, 2024. [Online]. Available: https:\/\/www.who.int\/health-topics\/cervical-cancer#tab=tab_1"},{"key":"3174_CR3","doi-asserted-by":"publisher","unstructured":"Mazeron JJ, Gerbaulet A. The centenary of discovery of radium. Radiother Oncol. Dec. 1998;49(3):205\u201316. https:\/\/doi.org\/10.1016\/S0167-8140(98)00143-1.","DOI":"10.1016\/S0167-8140(98)00143-1"},{"key":"3174_CR4","doi-asserted-by":"publisher","unstructured":"Mekuria M et al. Prevalence of cervical cancer and associated factors among women attended cervical cancer screening center at Gahandi Memorial Hospital, Ethiopia, Cancer Inform. 2021;20. https:\/\/doi.org\/10.1177\/11769351211068431","DOI":"10.1177\/11769351211068431"},{"key":"3174_CR5","doi-asserted-by":"publisher","unstructured":"Lozano R. Comparison of computer-assisted and manual screening of cervical cytology. Gynecol Oncol. Jan. 2007;104(1):134\u20138. https:\/\/doi.org\/10.1016\/j.ygyno.2006.07.025.","DOI":"10.1016\/j.ygyno.2006.07.025"},{"key":"3174_CR6","doi-asserted-by":"publisher","unstructured":"Shahid AH, Singh MP. A deep learning approach for prediction of parkinson\u2019s disease progression. Biomed Eng Lett. May 2020;10(2):227\u201339. https:\/\/doi.org\/10.1007\/s13534-020-00156-7.","DOI":"10.1007\/s13534-020-00156-7"},{"key":"3174_CR7","doi-asserted-by":"publisher","unstructured":"Battula KP, Chandana BS. Deep learning based cervical cancer classification and segmentation from pap smears images using an EfficientNet. Int. J. Adv. Comput. Sci. Appl. Dec. 2022;13(9):899\u2013908. https:\/\/doi.org\/10.14569\/IJACSA.2022.01309104","DOI":"10.14569\/IJACSA.2022.01309104"},{"key":"3174_CR8","doi-asserted-by":"publisher","first-page":"24219","DOI":"10.1109\/ACCESS.2020.2970121","volume":"8","author":"P Huang","year":"2020","unstructured":"Huang P, et al. Classification of cervical biopsy images based on LASSO and EL-SVM. IEEE Access. 2020;8:24219\u201328. https:\/\/doi.org\/10.1109\/ACCESS.2020.2970121.","journal-title":"IEEE Access"},{"key":"3174_CR9","doi-asserted-by":"publisher","unstructured":"Chandran V, et al. Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images. Biomed Res Int. 2021;2021. https:\/\/doi.org\/10.1155\/2021\/5584004.","DOI":"10.1155\/2021\/5584004"},{"key":"3174_CR10","doi-asserted-by":"publisher","first-page":"116925","DOI":"10.1109\/ACCESS.2019.2936017","volume":"7","author":"A Kurnianingsih","year":"2019","unstructured":"Kurnianingsih A, Allehaibi KHS, Nugroho LE, Widyawan L, Lazuardi L, Prabuwono AS, Mantoro T. Segmentation and classification of cervical cells using deep learning. IEEE Access. 2019;7:116925\u201341. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936017.","journal-title":"IEEE Access"},{"key":"3174_CR11","doi-asserted-by":"crossref","unstructured":"Lalasa M, Thomas J. A review of deep learning methods in cervical cancer detection, in Proc. Int. Conf. Soft Comput. Pattern Recognit., Cham: Springer Nature Switzerland. Dec. 2022; pp. 624\u2013633.","DOI":"10.1007\/978-3-031-27524-1_60"},{"key":"3174_CR12","doi-asserted-by":"publisher","unstructured":"Namalinzi F, Galadima KR, Nalwanga R, Sekitoleko I, Uwimbabazi LFR. Prediction of precancerous cervical cancer lesions among women living with HIV on antiretroviral therapy in Uganda: a comparison of supervised machine learning algorithms. BMC Womens Health. Dec. 2024;24(1). https:\/\/doi.org\/10.1186\/s12905-024-03232-7","DOI":"10.1186\/s12905-024-03232-7"},{"key":"3174_CR13","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2147\/MDER.S366303","volume":"15","author":"LW Habtemariam","year":"2022","unstructured":"Habtemariam LW, Zewde ET, Simegn GL. Cervix type and cervical cancer classification system using deep learning techniques. Med Devices (Auckl). 2022;15:163\u201376. https:\/\/doi.org\/10.2147\/MDER.S366303.","journal-title":"Med Devices (Auckl)"},{"key":"3174_CR14","doi-asserted-by":"publisher","unstructured":"Mohammed MA, Ali AM. Enhanced cancer subclassification using multi-omics clustering and quantum cat swarm optimization, Iraqi J. Comput. Sci. Math. 2024;5(3), Art. no. 37. https:\/\/doi.org\/10.52866\/ijcsm.2024.05.03.035","DOI":"10.52866\/ijcsm.2024.05.03.035"},{"key":"3174_CR15","doi-asserted-by":"publisher","unstructured":"Ali M, Mohammed MA. Optimized cancer subtype classification and clustering using cat swarm optimization and support vector machine approach for multi-omics data, J. Soft Comput. Data Min. Dec. 2024;5(2): pp. 223\u2013244. https:\/\/doi.org\/10.30880\/jscdm.2024.05.02.017","DOI":"10.30880\/jscdm.2024.05.02.017"},{"key":"3174_CR16","doi-asserted-by":"crossref","unstructured":"Ali AM, Mohammed MA. A comprehensive review of artificial intelligence approaches in omics data processing: evaluating progress and challenges. Int. J. Math. Stat. Comput. Sci. Dec. 2023;2: pp. 114\u2013167.","DOI":"10.59543\/ijmscs.v2i.8703"},{"key":"3174_CR17","doi-asserted-by":"publisher","unstructured":"Ming Y, Dong X, Zhao J, Chen Z, Wang H, Wu N. Deep learning-based multimodal image analysis for cervical cancer detection. Methods. Sep. 2022;205:46\u201352. https:\/\/doi.org\/10.1016\/j.ymeth.2022.05.004.","DOI":"10.1016\/j.ymeth.2022.05.004"},{"key":"3174_CR18","doi-asserted-by":"publisher","unstructured":"Abinaya K, Sivakumar B. A deep learning based approach for cervical cancer classification using 3D CNN and vision transformer. J Imaging Inf Med. Jan. 2024;37:280\u201396. https:\/\/doi.org\/10.1007\/s10278-023-00911-z.","DOI":"10.1007\/s10278-023-00911-z"},{"issue":"1","key":"3174_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-025-01152-3","volume":"12","author":"CJ Ejiyi","year":"2025","unstructured":"Ejiyi CJ, Cai D, Eze FO, Ejiyi MB, Idoko JE, Asere SK, Ejiyi TU. Polynomial-SHAP as a SMOTE alternative in conglomerate neural networks for realistic data augmentation in cardiovascular and breast cancer diagnosis. J Big Data. 2025;12(1):1\u201328. https:\/\/doi.org\/10.1186\/s40537-025-01152-3.","journal-title":"J Big Data"},{"key":"3174_CR20","doi-asserted-by":"publisher","first-page":"100166","DOI":"10.1016\/j.health.2023.100166","volume":"3","author":"CJ Ejiyi","year":"2023","unstructured":"Ejiyi CJ, Qin Z, Amos J, Ejiyi MB, Nnani A, Ejiyi TU, Agbesi VK, Diokpo C, Okpara C. A robust predictive diagnosis model for diabetes mellitus using Shapley-incorporated machine learning algorithms. Healthc Anal. 2023;3:100166. https:\/\/doi.org\/10.1016\/j.health.2023.100166.","journal-title":"Healthc Anal"},{"key":"3174_CR21","doi-asserted-by":"publisher","unstructured":"L\u00f3pez DM, Blobel B, Hullin C. Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence. Front. Med. 2022;9, Art. no. 958097. https:\/\/doi.org\/10.3389\/fmed.2022.958097","DOI":"10.3389\/fmed.2022.958097"},{"issue":"4","key":"3174_CR22","doi-asserted-by":"publisher","first-page":"e1312","DOI":"10.1002\/widm.1312","volume":"9","author":"A Holzinger","year":"2019","unstructured":"Holzinger A, Langs G, Denk H, Zatloukal K, M\u00fcller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):e1312. https:\/\/doi.org\/10.1002\/widm.1312.","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"3174_CR23","doi-asserted-by":"publisher","unstructured":"Zeng M, Zou B, Wei F, Liu X, Wang L. Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data, in Proc. IEEE Int. Conf. Online Anal. Comput. Sci. (ICOACS). 2016: pp. 225\u2013228, Sep. https:\/\/doi.org\/10.1109\/ICOACS.2016.7563084","DOI":"10.1109\/ICOACS.2016.7563084"},{"key":"3174_CR24","doi-asserted-by":"publisher","unstructured":"Senan EM, et al. Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques. J Healthc Eng. 2021;2021. https:\/\/doi.org\/10.1155\/2021\/1004767.","DOI":"10.1155\/2021\/1004767"},{"issue":"2","key":"3174_CR25","first-page":"317","volume":"10","author":"L Mukku","year":"2024","unstructured":"Mukku L, Thomas J, CMT-CNN. Colposcopic multimodal Temporal hybrid deep learning model to detect cervical intraepithelial neoplasia. Int J Adv Intell Inf. 2024;10(2):317\u201332.","journal-title":"Int J Adv Intell Inf"},{"key":"3174_CR26","doi-asserted-by":"crossref","unstructured":"Mukku L, Thomas J. Multimodal early fusion strategy based on deep learning methods for cervical cancer identification, in Proc. Congr. Intell. Syst. Singapore: Springer. Sep. 2023; pp. 109\u2013118.","DOI":"10.1007\/978-981-99-9043-6_9"},{"key":"3174_CR27","unstructured":"Russakovsky O et al. Sep., ImageNet large scale visual recognition challenge, 2014. Accessed: Dec. 07, 2024. [Online]. Available: http:\/\/arxiv.org\/abs\/1409.0575"},{"key":"3174_CR28","doi-asserted-by":"publisher","unstructured":"Ma J, Fan X, Yang SX, Zhang X, Zhu X. Contrast limited adaptive histogram equalization-based fusion in YIQ and HSI color spaces for underwater image enhancement. Int J Pattern Recognit Artif Intell. Jul. 2018;32(7). https:\/\/doi.org\/10.1142\/S0218001418540186.","DOI":"10.1142\/S0218001418540186"},{"key":"3174_CR29","unstructured":"Building powerful image classification models using very little data. Accessed: Dec. 07, 2024. [Online]. Available: https:\/\/blog.keras.io\/building-powerful-image-classification-models-using-very-little-data.html"},{"key":"3174_CR30","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. [Online]. Available: http:\/\/image-net.org\/challenges\/LSVRC\/2015\/","DOI":"10.1109\/CVPR.2016.90"},{"key":"3174_CR31","unstructured":"Team K. GridSearch Tuner, Keras Team. Accessed: Nov. 06, 2024. [Online]. Available: https:\/\/keras.io\/api\/keras_tuner\/tuners\/grid\/"},{"key":"3174_CR32","doi-asserted-by":"publisher","unstructured":"Razali N, Mostafa SA, Mustapha A, Wahab MHA, Ibrahim NA. Risk factors of cervical cancer using classification in data mining. J Phys Conf Ser. Jun. 2020;1529(2). https:\/\/doi.org\/10.1088\/1742-6596\/1529\/2\/022102.","DOI":"10.1088\/1742-6596\/1529\/2\/022102"},{"key":"3174_CR33","doi-asserted-by":"publisher","unstructured":"Chen T et al. Apr., Multi-modal fusion learning for cervical dysplasia diagnosis, in Proc. IEEE Int. Symp. Biomed. Imaging (ISBI). 2019-Apr: pp. 1505\u20131509. https:\/\/doi.org\/10.1109\/ISBI.2019.8759303","DOI":"10.1109\/ISBI.2019.8759303"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03174-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03174-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03174-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T10:59:19Z","timestamp":1756724359000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03174-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3174"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03174-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"5 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study complies with the principles of the Declaration of Helsinki and was approved by the Jimma Institute of Technology, Research, and Ethical Review Board (IRB Reference No. RPD\/JiT\/183\/15). The data used in this research were collected retrospectively from hospital patient card sheets and histopathological image archives. The need for informed consent was waived by the Jimma Institute of Technology, Research, and Ethical Review Board, by national research ethics regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"322"}}