ArticleComputational and mathematical methods in medicine2022
Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting cervical cancer among women living with HIV/AIDS at public health facilities in a resource-limited setting in Ethiopia using machine learning analysis.Discover oncology · 2026Article
- Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.Scientific reports · 2026Article
- Underwater image enhancement using colour balancing and morphological residual processing through gamma correction.Scientific reports · 2026Article
- Recent Advances in Nanocarrier-Based Drug Delivery Systems for Lung Cancer.International journal of nanomedicine · 2026Review
- CERV-Score: A Hybrid Machine Learning Framework for Cervical Cancer Risk Prediction Using Integrated Clinical and Genomic Data.International journal of telemedicine and applications · 2026Article
- Article
- Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics.Scientific reports · 2025Article
- Evaluating the Cost-Effectiveness of Cervical Cancer Screening and Treatment in Western Romania.Current oncology (Toronto, Ont.) · 2025Article
- CERVIXNET: An Efficient Approach for the Detection and Classifications of the Cervigram Images Using Modified Deep Learning Architecture.Current medical imaging · 2025Article
- Mechanistic studies and therapeutic potential of angiopoietin in head and neck tumor angiogenesis.Frontiers in oncology · 2025Review
- A lightweight deep learning method to identify different types of cervical cancer.Scientific reports · 2024Article
- Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy.Scientific reports · 2024Article
- Mesoporous silica nanoparticles: a versatile carrier platform in lung cancer management.Nanomedicine (London, England) · 2024Review
- Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning.Scientific reports · 2024Article
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision,
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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.