ArticleJournal of personalized medicine2022
Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Integrating multi-omics and clinical features to model survival in epithelial ovarian cancer subtypes.Scientific reports · 2025Article
- Review: Detection of Cancer Biomarkers from a Clinical Perspective.International journal of molecular sciences · 2025Review
- Application of multimodal integration to develop preoperative diagnostic models for borderline and malignant ovarian tumors.Scientific reports · 2025Article
- Machine-Learning-Assisted Analysis of Patient Clinical Biomarkers to Improve Ovarian Cancer Diagnosis.Precision chemistry · 2025Article
- Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images.Cancers · 2025Article
- Early prediction and risk stratification of ovarian cancer based on clinical data using machine learning approaches.Journal of gynecologic oncology · 2025Article
- Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.Discover oncology · 2025Article
- Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods.Frontiers in public health · 2025Article
- Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer.Scientific reports · 2024Article
- Prediction of ovarian cancer using artificial intelligence tools.Health science reports · 2024Article
- Predicting Dropout From Cognitive Behavioral Therapy for Panic Disorder Using Machine Learning Algorithms.Journal of clinical medicine research · 2024Article
- Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model.Bioengineering (Basel, Switzerland) · 2024Article
- Screening ovarian cancer by using risk factors: machine learning assists.Biomedical engineering online · 2024Article
- RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis.Tomography (Ann Arbor, Mich.) · 2024Article
- Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients.Cancer biomarkers : section A of Disease markers · 2024Article
- Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.Cancers · 2023Article
- Identifying Explainable Machine Learning Models and a Novel SFRP2International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student's
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.