This paper is in the following e-collection/theme issue:

Artificial Intelligence (2144) Machine Learning (2083) Pregnancy Information, Education and Lifestyle Interventions (405)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28856, first published .
Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Authors of this article:

Zahid Ullah1 Author Orcid Image ;   Farrukh Saleem1 Author Orcid Image ;   Mona Jamjoom2 Author Orcid Image ;   Bahjat Fakieh1 Author Orcid Image
Article Authors Cited by (29) Tweetations (2) Metrics

Journals

  1. Alsayed A, Rahim M, AlBidewi I, Hussain M, Jabeen S, Alromema N, Hussain S, Jibril M. Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques. Applied Sciences 2021;11(22):10639 View
  2. Jamjoom M, Ahmed N, Abbas S, Hodhod R, El-Sheikh M, Ullah Z. A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent Agents. Electronics 2023;12(4):970 View
  3. Zare S, Meidani Z, Ouhadian M, Akbari H, Zand F, Fakharian E, Sharifian R. Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems. BMC Health Services Research 2022;22(1) View
  4. Ullah Z, Jamjoom M. A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques. PeerJ Computer Science 2023;9:e1234 View
  5. Ullah Z, Jamjoom M, Bashir A. [Retracted] Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features. Journal of Healthcare Engineering 2023;2023(1) View
  6. Hussain M, Cifci M, Sehar T, Nabi S, Cheikhrouhou O, Maqsood H, Ibrahim M, Mohammad F. Machine learning based efficient prediction of positive cases of waterborne diseases. BMC Medical Informatics and Decision Making 2023;23(1) View
  7. Ullah Z, Saleem F, Jamjoom M, Fakieh B, Kateb F, Ali A, Shah B, Javed A. Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods. Computational Intelligence and Neuroscience 2022;2022:1 View
  8. Ullah Z, Jamjoom M. An intelligent approach for Arabic handwritten letter recognition using convolutional neural network. PeerJ Computer Science 2022;8:e995 View
  9. Baltzer A, Casadonte R, Korff A, Baltzer L, Kriegsmann K, Kriegsmann M, Kriegsmann J. Biological injection therapy with leukocyte-poor platelet-rich plasma induces cellular alterations, enhancement of lubricin, and inflammatory downregulation in vivo in human knees: A controlled, prospective human clinical trial based on mass spectrometry imaging analysis. Frontiers in Surgery 2023;10 View
  10. S. H, V. M. An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction. Artificial Intelligence in Medicine 2023;143:102621 View
  11. Shearah Z, Ullah Z, Fakieh B. Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms. Diagnostics 2023;13(20):3204 View
  12. Hu J, Yang X, Ren J, Zhong S, Fan Q, Li W. Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis. Toxicology Mechanisms and Methods 2023;33(9):781 View
  13. Zhang Z, Huang Y, Liu G, Yu W, Xie Q, Chen Z, Huang G, Wei J, Zhang H, Chen D, Du H. Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma. Scientific Reports 2024;14(1) View
  14. Coutinho-Almeida J, Cardoso A, Cruz-Correia R, Pereira-Rodrigues P. Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study. JMIR Formative Research 2024;8:e54109 View
  15. Ullah Z, Jamjoom M, Thirumalaisamy M, Alajmani S, Saleem F, Sheikh-Akbari A, Khan U. A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor. Biomedical Engineering and Computational Biology 2024;15 View
  16. Khadidos A, Saleem F, Selvarajan S, Ullah Z, Khadidos A. Ensemble machine learning framework for predicting maternal health risk during pregnancy. Scientific Reports 2024;14(1) View
  17. Fakieh B, Saleem F. COVID-19 from symptoms to prediction: A statistical and machine learning approach. Computers in Biology and Medicine 2024;182:109211 View
  18. Bai J, Kang X, Wang W, Yang Z, Ou W, Huang Y, Lu Y. A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system. DIGITAL HEALTH 2024;10 View
  19. Cui H, Shan W, Na Q, Liu T. Models for predicting vaginal birth after cesarean section: scoping review. BMC Pregnancy and Childbirth 2024;24(1) View
  20. Owusu-Adjei M, Ben Hayfron-Acquah J, Frimpong T, Gaddafi A, Villanueva C. An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. PLOS Digital Health 2025;4(2):e0000543 View
  21. Bennett R, Pierce S, Razzaghi T. Interpretable Machine Learning Models for Predicting Cesarean Delivery in Class III Obese Cohorts. IEEE Access 2025;13:41230 View
  22. Althaqafi T, Saleem F, AL-Ghamdi A. Enhancing student performance prediction: the role of class imbalance handling in machine learning models. Discover Computing 2025;28(1) View
  23. Sindhu P, Rao P. Latent feature discovery with kernel-PCA and random forest for childbirth method classification. International Journal of Information Technology 2025;17(8):4659 View
  24. Zamorano M, Gómez M, Castejon C, Carboni M. Analysis of Acoustic Emission Waveforms by Wavelet Packet Transform for the Detection of Crack Initiation Due to Fretting Fatigue in Solid Railway Axles. Applied Sciences 2025;15(15):8435 View
  25. Zhou Y, Li J, Hou X, Li Z, Xu Y, Wang Y, Sun M, Zheng F, Guo E, Zhou J. Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions. Clinical and Experimental Obstetrics & Gynecology 2025;52(7) View

Conference Proceedings

  1. S H, V M. 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI). A Comprehensive Analysis on Various Machine Learning Algorithms for Child Birth Mode Prediction View
  2. Ukrit M, Jeyavathana R, Rani A, Chandana V. 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE). Maternal Health Risk Prediction with Machine Learning Methods View
  3. Jagtap K, Badshah M, Pradhan S, Kulkarni R, Bhanap S. 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). Pregnancy mode Detection Predictive Model for Enhanced Maternal Healthcare View
  4. Jena L, Swain M, Panda D, Jena B, Kamila N. 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). An Intelligent Predictive Model for Caesarean Section Risk Classification: A Comparative Study of Biologically Inspired Algorithms View