{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T22:36:55Z","timestamp":1768516615224,"version":"3.49.0"},"reference-count":52,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Nowadays, caesarean section (CS) is given preference over vaginal birth and this trend is rapidly rising around the globe, although CS has serious complications such as pregnancy scar, scar dehiscence, and morbidly adherent placenta. Thus, CS should only be performed when it is absolutely necessary for mother and fetus. To avoid unnecessary CS, researchers have developed different machine-learning- (ML-) based clinical decision support systems (CDSS) for CS prediction using electronic health record of the pregnant women. However, previously proposed methods suffer from the problems of poor accuracy and biasedness in ML. To overcome these problems, we have designed a novel CDSS where random oversampling example (ROSE) technique has been used to eliminate the problem of minority classes in the dataset. Furthermore, principal component analysis has been employed for feature extraction from the dataset while, for classification purpose, random forest (RF) model is deployed. We have fine-tuned the hyperparameter of RF using a grid search algorithm for optimal classification performance. Thus, the newly proposed system is named ROSE-PCA-RF and it is trained and tested using an online CS dataset available on the UCI repository. In the first experiment, conventional RF model is trained and tested on the dataset while in the second experiment, the proposed model is tested. The proposed ROSE-PCA-RF model improved the performance of traditional RF by 4.5% with reduced time complexity, while only using two extracted features through the PCA. Moreover, the proposed model has obtained 96.29% accuracy on training data while improving the accuracy of 97.12% on testing data.<\/jats:p>","DOI":"10.1155\/2022\/1901735","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T02:05:21Z","timestamp":1654567521000},"page":"1-13","source":"Crossref","is-referenced-by-count":20,"title":["A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4190-3532","authenticated-orcid":true,"given":"Ashir","family":"Javeed","sequence":"first","affiliation":[{"name":"Aging Research Center, Karolinska Institute, Solna, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3095-7271","authenticated-orcid":true,"given":"Liaqat","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Science and Technology Bannu, Bannu, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3672-6132","authenticated-orcid":true,"given":"Abegaz","family":"Mohammed Seid","sequence":"additional","affiliation":[{"name":"Information & Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9998-9183","authenticated-orcid":true,"given":"Arif","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dilpazir","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9901-4909","authenticated-orcid":true,"given":"Yakubu","family":"Imrana","sequence":"additional","affiliation":[{"name":"School of Engineering, University for Development Studies, Tamale, Ghana"},{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","first-page":"63","article-title":"Risk indicators for cesarean section due to cephalopelvic disproportion in lamphun hospital","volume":"88","author":"S. Khunpradit","year":"2005","journal-title":"Journal of the Medical Association of Thailand"},{"key":"2","first-page":"158","article-title":"Risk factors of cesarean delivery due to cephalopelvic","author":"W. Wianwiset","year":"2011","journal-title":"Thai Journal of Obstetrics and Gynaecology"},{"issue":"2","key":"3","article-title":"Knowledge and attitude of pregnant women to caesarean section in a semi-urban community in northwest Nigeria","volume":"3","author":"A. Ao","year":"2013","journal-title":"Journal of the West African College of Surgeons"},{"key":"4","article-title":"Caesarean section rates continue to rise, amid growing inequalities in access","author":"World Health Organization (Who)","year":"2021"},{"key":"5","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1111\/1471-0528.12633","article-title":"Maternal complications and perinatal mortality: findings of the world health organization multicountry survey on maternal and newborn health","volume":"121","author":"J. P. Vogel","year":"2014","journal-title":"BJOG: An International Journal of Obstetrics and Gynaecology"},{"issue":"1","key":"6","article-title":"Development of caesarean section prediction models: secondary analysis of a prospective cohort study in two sub-saharan african countries","volume":"16","author":"H. C. d. Souza","year":"2019","journal-title":"Reproductive Health"},{"key":"7","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/6462657","article-title":"Data mining in employee healthcare detection using intelligence techniques for industry development","volume":"2022","author":"A. Mehbodniya","year":"2022","journal-title":"Journal of Healthcare Engineering"},{"key":"8","article-title":"Reliable Parkinson\u2019s disease detection by analyzing handwritten drawings: construction of an unbiased cascaded learning system based on feature selection and adaptive boosting model","volume":"7","author":"L. Ali","year":"2019","journal-title":"IEEE Access"},{"key":"9","article-title":"Mmdd-ensemble: a multimodal data\u2013driven ensemble approach for Parkinson\u2019s disease detection","volume":"15","author":"L. Ali","year":"2021","journal-title":"Frontiers in Neuroscience"},{"issue":"22\u201328","key":"10","article-title":"Early diagnosis of Parkinson\u2019s disease from multiple voice recordings by simultaneous sample and feature selection","volume":"137","author":"L. Ali","year":"2019","journal-title":"Expert Systems with Applications"},{"issue":"1\u201310","key":"11","article-title":"Automated detection of Parkinson\u2019s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network","volume":"7","author":"L. Ali","year":"2019","journal-title":"IEEE journal of translational engineering in health and medicine"},{"key":"12","article-title":"A multi-model framework for evaluating type of speech samples having complementary information about Parkinson\u2019s disease","author":"L. Ali"},{"key":"13","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-16-8048-9_1","article-title":"An integrated system for unbiased Parkinson\u2019s disease detection from handwritten drawings","volume-title":"Advances in Intelligent Systems and Computing","author":"L. Ali","year":"2022"},{"key":"14","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/8870240","article-title":"Development of hepatitis disease detection system by exploiting sparsity in linear support vector machine to improve strength of adaboost ensemble model","volume":"2020","author":"W. Akbar","year":"2020","journal-title":"Mobile Information Systems"},{"issue":"17","key":"15","doi-asserted-by":"crossref","DOI":"10.1007\/s00521-020-04870-2","article-title":"Lung nodules detection using semantic segmentation and classification with optimal features","volume":"33","author":"T. Meraj","year":"2021","journal-title":"Neural Computing & Applications"},{"key":"16","article-title":"A feature-driven decision support system for heart failure prediction based on statistical model and Gaussian naive bayes","volume":"2019","author":"L. Ali","year":"2019","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"7","key":"17","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1007\/s00521-020-05157-2","article-title":"Lda\u2013ga\u2013svm: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine","volume":"33","author":"L. Ali","year":"2021","journal-title":"Neural Computing & Applications"},{"key":"18","article-title":"An optimized stacked support vector machines based expert system for the effective prediction of heart failure","volume":"7","author":"L. Ali","year":"2019","journal-title":"IEEE Access"},{"key":"19","article-title":"An automated diagnostic system for heart disease prediction based on chi sequare statistical model and optimally configured deep neural network","volume":"7","author":"L. Ali","year":"2019","journal-title":"IEEE Access"},{"key":"20","doi-asserted-by":"crossref","article-title":"Performance analysis of pca, sparse pca, kernel pca and incremental pca algorithms for heart failure prediction","author":"A. Rehman","DOI":"10.1109\/ICECCE49384.2020.9179199"},{"key":"21","article-title":"Heart risk failure prediction using a novel feature selection method for feature refinement and neural network for classification","volume-title":"Mobile Information Systems","author":"A. Javeed","year":"2020"},{"key":"22","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2019.2952107","article-title":"An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection","volume":"7","author":"A. Javeed","year":"2019","journal-title":"IEEE Access"},{"issue":"5","key":"23","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.irbm.2020.04.003","article-title":"An approach based on mutually informed neural networks to optimize the generalization capabilities of decision support systems developed for heart failure prediction","volume":"42","author":"L. Ali","year":"2021","journal-title":"Irbm"},{"issue":"4","key":"24","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1097\/TA.0000000000002888","article-title":"A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit","volume":"89","author":"F. S. Ahmed","year":"2020","journal-title":"Journal of Trauma and Acute Care Surgery"},{"issue":"3","key":"25","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1007\/s12652-020-02456-3","article-title":"A hybrid machine learning framework to predict mortality in paralytic ileus patients using electronic health records (ehrs)","volume":"12","author":"F. S. Ahmad","year":"2021","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"6","key":"26","article-title":"Application of decision tree algorithm for data mining in healthcare operations: a case study","volume":"52","author":"F. S. Gharehchopogh","year":"2012","journal-title":"International Journal of Computer Application"},{"key":"27","article-title":"Machine learning for sub-population assessment: evaluating the c-section rate of different physician practices","author":"R. Caruana"},{"key":"28","first-page":"3835","article-title":"The challenge of using soft computing for decision support during labour","author":"C. D. Stylios"},{"key":"29","article-title":"Predicting preterm birth is not elusive: machine learning paves the way to individual wellness","author":"I. Vovsha"},{"issue":"12","key":"30","article-title":"Determinants of cesarean section among primiparas: a comparison of classification methods","volume":"47","author":"S. Maroufizadeh","year":"2018","journal-title":"Iranian Journal of Public Health"},{"key":"31","article-title":"The baby delivery method estimation using na\u2019\u0301\u0131ve bayes classification model for mobile application","author":"E. N. Iftitah"},{"key":"32","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2019.00246","article-title":"A strategy for classification of \u201cvaginal vs. cesarean section\u201d delivery: bivariate empirical mode decomposition of cardiotocographic recordings","volume":"10","author":"S. Saleem","year":"2019","journal-title":"Frontiers in Physiology"},{"key":"33","volume-title":"Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Healthcare Operational Decisions","author":"M. Amin","year":"2018"},{"key":"34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/978-3-030-54932-9_13","article-title":"Risk prediction with machine learning in cesarean section: optimizing healthcare operational decisions","volume-title":"Signal Processing Techniques for Computational Health Informatics","author":"S. Rahman","year":"2021"},{"issue":"2","key":"35","article-title":"Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section","volume":"5","author":"A. A. Abdillah","year":"2021","journal-title":"Eastern-European Journal of Enterprise Technologies"},{"key":"36","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1080\/24699322.2019.1649074","article-title":"Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis","volume":"24","author":"J. Zhang","year":"2019","journal-title":"Computer Assisted Surgery"},{"key":"37","article-title":"UCI Machine Learning Repository","author":"UCI","year":"2021"},{"key":"38","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.bspc.2017.06.015","article-title":"An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson\u2019s disease","volume":"38","author":"Y. Wang","year":"2017","journal-title":"Biomedical Signal Processing and Control"},{"key":"39","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115524","article-title":"A bidirectional lstm deep learning approach for intrusion detection","volume":"185","author":"Y. Imrana","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"40","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/JBHI.2013.2244902","article-title":"Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability","volume":"17","author":"P. Melillo","year":"2013","journal-title":"IEEE journal of biomedical and health informatics"},{"issue":"5","key":"41","article-title":"\u03c7 2-bidlstm: a feature driven intrusion detection system based on \u03c7 2 statistical model and bidirectional lstm","volume":"22","author":"Yakubu Imrana","year":"2022","journal-title":"Sensors"},{"issue":"10","key":"42","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1016\/S0031-3203(03)00044-X","article-title":"Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition","volume":"36","author":"X. Wang","year":"2003","journal-title":"Pattern Recognition"},{"issue":"7","key":"43","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1111\/trf.14639","article-title":"Principal component analysis","volume":"58","author":"A. S. Hess","year":"2018","journal-title":"Transfusion"},{"issue":"5","key":"44","first-page":"126","article-title":"Number of trees in random forest","volume":"51","author":"M. Liu","year":"2015","journal-title":"Computer engineering and applications"},{"key":"45","first-page":"237","article-title":"Osteoarthritis disease prediction based on random forest","author":"U. Aprilliani"},{"key":"46","volume-title":"Data Mining: Concepts and Techniques","author":"J. Han","year":"2011"},{"issue":"3","key":"47","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/uog.7315","article-title":"Validation of models that predict cesarean section after induction of labor","volume":"34","author":"C. J. M. Verhoeven","year":"2009","journal-title":"Ultrasound in Obstetrics and Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology"},{"issue":"5\u201314","key":"48","article-title":"Predicting caesarean section by applying nearest neighbor analysis","volume":"31","author":"S. Sodsee","year":"2014","journal-title":"Procedia Computer Science"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.21817\/indjcse\/2018\/v9i6\/180906012"},{"issue":"16","key":"50","article-title":"Artificial neural network analysis of spontaneous preterm labor and birth and its major determinants","volume":"34","author":"K.-S. Lee","year":"2019","journal-title":"Journal of Korean Medical Science"},{"key":"51","first-page":"331","article-title":"Prediction of cesarean childbirth using ensemble machine learning methods","author":"N. I. Khan"},{"key":"52","doi-asserted-by":"publisher","DOI":"10.1080\/14767058.2020.1837769"}],"updated-by":[{"DOI":"10.1155\/2023\/9843917","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"record-id":"60295"},{"DOI":"10.1155\/2023\/9843917","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000}}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2022\/1901735.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2022\/1901735.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2022\/1901735.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T02:05:50Z","timestamp":1654567550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2022\/1901735\/"}},"subtitle":[],"editor":[{"given":"Muhammad Zubair","family":"Asghar","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":52,"alternative-id":["1901735","1901735"],"URL":"https:\/\/doi.org\/10.1155\/2022\/1901735","relation":{"retraction":[{"id-type":"doi","id":"10.1155\/2023\/9843917","asserted-by":"object"}]},"ISSN":["1687-5273","1687-5265"],"issn-type":[{"value":"1687-5273","type":"electronic"},{"value":"1687-5265","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}