{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T07:43:57Z","timestamp":1790927037349,"version":"4.1.0"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Placenta"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.placenta.2025.03.003","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T16:54:31Z","timestamp":1741107271000},"page":"33-42","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Prediction of clinical risk factors in pregnancy using optimized neural network scheme"],"prefix":"10.1016","volume":"163","author":[{"given":"G.","family":"Bhavani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Jeyalakshmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.placenta.2025.03.003_bib1","first-page":"1031","article-title":"Microbial changes during pregnancy, birth, and infancy","author":"Nuriel-Ohayon","year":"2016","journal-title":"Front. Microbiol."},{"issue":"8","key":"10.1016\/j.placenta.2025.03.003_bib2","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1111\/obr.12288","article-title":"Risks associated with obesity in pregnancy, for the mother and baby: a systematic review of reviews","volume":"16","author":"Marchi","year":"2015","journal-title":"Obes. Rev."},{"issue":"5","key":"10.1016\/j.placenta.2025.03.003_bib3","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1038\/nrendo.2016.37","article-title":"Micronutrient deficiencies in pregnancy worldwide: health effects and prevention","volume":"12","author":"Gernand","year":"2016","journal-title":"Nat. Rev. Endocrinol."},{"issue":"6","key":"10.1016\/j.placenta.2025.03.003_bib4","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.placenta.2013.03.005","article-title":"Association of placental T2 relaxation times and uterine artery Doppler ultrasound measures of placental blood flow","volume":"34","author":"Derwig","year":"2013","journal-title":"Placenta"},{"issue":"6","key":"10.1016\/j.placenta.2025.03.003_bib5","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1002\/uog.20272","article-title":"ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth","volume":"53","author":"Salomon","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"1","key":"10.1016\/j.placenta.2025.03.003_bib6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/pd.6059","article-title":"Exploring a new paradigm for the fetal anomaly ultrasound scan: artificial intelligence in real time","volume":"42","author":"Matthew","year":"2022","journal-title":"Prenat. Diagn."},{"issue":"9","key":"10.1016\/j.placenta.2025.03.003_bib7","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.3390\/diagnostics12092229","article-title":"Ensemble transfer learning for fetal head analysis: from segmentation to gestational age and weight prediction","volume":"12","author":"Alzubaidi","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.placenta.2025.03.003_bib8","series-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization","first-page":"1","article-title":"Automated segmentation of standard scanning planes to measure biometric parameters in foetal ultrasound images\u2013a survey","author":"Balagalla","year":"2023"},{"key":"10.1016\/j.placenta.2025.03.003_bib9","series-title":"Audio Engineering Society Conference: 2018 AES International Conference on Audio for Virtual and Augmented Reality","article-title":"Comparison of head-related transfer functions pre-processing techniques for spherical harmonics decomposition","author":"Brinkmann","year":"2018"},{"key":"10.1016\/j.placenta.2025.03.003_bib10","series-title":"2017 International Conference on Intelligent Computing and Control Systems (ICICCS)","first-page":"492","article-title":"Predictive analytics in health care using machine learning tools and techniques","author":"Nithya","year":"2017"},{"key":"10.1016\/j.placenta.2025.03.003_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.biopha.2021.112183","article-title":"Gestational diabetes mellitus-A metabolic and reproductive disorder","volume":"143","author":"Choudhury","year":"2021","journal-title":"Biomed. Pharmacother."},{"issue":"4","key":"10.1016\/j.placenta.2025.03.003_bib12","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1002\/uog.20391","article-title":"Two\u2010stage approach for prediction of small\u2010for\u2010gestational\u2010age neonate and adverse perinatal outcome by routine ultrasound examination at 35\u201337 weeks' gestation","volume":"54","author":"Akolekar","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"8","key":"10.1016\/j.placenta.2025.03.003_bib13","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0221202","article-title":"Prediction model development of late-onset preeclampsia using machine learning-based methods","volume":"14","author":"Jhee","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.placenta.2025.03.003_bib14","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neunet.2023.02.020","article-title":"Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication","volume":"162","author":"Sufriyana","year":"2023","journal-title":"Neural Netw."},{"issue":"12","key":"10.1016\/j.placenta.2025.03.003_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2022.100655","article-title":"Early prediction and longitudinal modeling of preeclampsia from multiomics","volume":"3","author":"Mari\u0107","year":"2022","journal-title":"Patterns"},{"issue":"1","key":"10.1016\/j.placenta.2025.03.003_bib16","first-page":"1","article-title":"Fetalbirthweight prediction with measured data by a temporal machine learning method","volume":"21","author":"Tao","year":"2021","journal-title":"BMC Med. Inf. Decis. Making"},{"key":"10.1016\/j.placenta.2025.03.003_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2019.101748","article-title":"Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning","volume":"102","author":"Lu","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.placenta.2025.03.003_bib18","first-page":"1","article-title":"Optimisation algorithm in health care: review on the State-of-the-Art models","author":"Shivaprasad More","year":"2023","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"5","key":"10.1016\/j.placenta.2025.03.003_bib19","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1007\/s10115-022-01825-y","article-title":"An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer","volume":"65","author":"Kurman","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.placenta.2025.03.003_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105311","article-title":"A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems","volume":"115","author":"Kaya","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.placenta.2025.03.003_bib21","article-title":"An efficient high-dimensional gene selection approach based on binary horse herd optimization algorithm for biological data classification","author":"Mehrabi","year":"2023","journal-title":"arXiv preprint arXiv:2308.09791"},{"issue":"3","key":"10.1016\/j.placenta.2025.03.003_bib22","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3390\/a16030167","article-title":"Literature review on hybrid evolutionary approaches for feature selection","volume":"16","author":"Piri","year":"2023","journal-title":"Algorithms"},{"issue":"12","key":"10.1016\/j.placenta.2025.03.003_bib23","article-title":"Unwanted teenage pregnancy and its complications: a narrative review","volume":"14","author":"Chakole","year":"2022","journal-title":"Cureus"},{"key":"10.1016\/j.placenta.2025.03.003_bib24","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1186\/s12884-023-05819-8","article-title":"Fetal weight estimation based on deep neural network: a retrospective observational study","volume":"23","author":"Wang","year":"2023","journal-title":"BMC Pregnancy Childbirth"},{"key":"10.1016\/j.placenta.2025.03.003_bib25","article-title":"Karnet: kalman filter augmented recurrent neural network for learning world models in autonomous driving tasks","author":"Manjunatha","year":"2023","journal-title":"arXiv preprint arXiv:2305.14644"},{"key":"10.1016\/j.placenta.2025.03.003_bib26","series-title":"Fundamentals of Spherical Array Processing","author":"Rafaely","year":"2015"},{"key":"10.1016\/j.placenta.2025.03.003_bib27","first-page":"1","article-title":"Identification of duplicate parts of hyper spectral images based on fuzzy by dimensionality reduction techniques","author":"Pattem","year":"2023","journal-title":"Soft Comput."},{"issue":"14","key":"10.1016\/j.placenta.2025.03.003_bib28","doi-asserted-by":"crossref","first-page":"9139","DOI":"10.1007\/s00500-021-05875-2","article-title":"Difference sequence-based distance measure for intuitionistic fuzzy sets and its application in decision making process","volume":"25","author":"Ashraf","year":"2021","journal-title":"Soft Comput."},{"key":"10.1016\/j.placenta.2025.03.003_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106337","article-title":"A novel hybrid feature selection method based on dynamic feature importance","volume":"93","author":"Wei","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.placenta.2025.03.003_bib30","doi-asserted-by":"crossref","first-page":"5691","DOI":"10.1007\/s12652-020-01930-2","article-title":"Deep learning based big medical data analytic model for diabetes complication prediction","volume":"11","author":"Vidhya","year":"2020","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"10.1016\/j.placenta.2025.03.003_bib31","series-title":"Zitelli and Davis' Atlas of Pediatric Physical Diagnosis","first-page":"43","article-title":"General techniques of physical examination assessment of the newborn","author":"Balest","year":"2021"},{"key":"10.1016\/j.placenta.2025.03.003_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105015","article-title":"Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring","volume":"185","author":"Signorini","year":"2020","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"12","key":"10.1016\/j.placenta.2025.03.003_bib33","article-title":"Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound","volume":"5","author":"P\u0142otka","year":"2023","journal-title":"American journal of obstetrics &gynecology MFM"},{"issue":"1","key":"10.1016\/j.placenta.2025.03.003_bib34","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1002\/uog.19066","article-title":"Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models","volume":"52","author":"Hammami","year":"2018","journal-title":"Ultrasound Obstet. Gynecol."}],"container-title":["Placenta"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0143400425000633?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0143400425000633?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T10:05:27Z","timestamp":1743761127000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0143400425000633"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":34,"alternative-id":["S0143400425000633"],"URL":"https:\/\/doi.org\/10.1016\/j.placenta.2025.03.003","relation":{},"ISSN":["0143-4004"],"issn-type":[{"value":"0143-4004","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prediction of clinical risk factors in pregnancy using optimized neural network scheme","name":"articletitle","label":"Article Title"},{"value":"Placenta","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.placenta.2025.03.003","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}