ArticleJournal of medical systems2019
Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 116 citations in OpenAlex.
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based Risk Calculator with Explainable Artificial Intelligence (XAI).Infection and drug resistance · 2025Article
- Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.Journal of imaging informatics in medicine · 2024Article
- Article
- Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.Frontiers in oncology · 2024Review
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Microscopic image-based classification of adipocyte differentiation by machine learning.Histochemistry and cell biology · 2023Article
- Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.Neural processing letters · 2023Article
- Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network.Computers & electrical engineering : an international journal · 2022Article
- Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province.Frontiers in medicine · 2022Article
- Using random forest algorithm for glomerular and tubular injury diagnosis.Frontiers in medicine · 2022Article
- Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem.Medicina (Kaunas, Lithuania) · 2021Article
- Genomic sequence analysis of lung infections using artificial intelligence technique.Interdisciplinary sciences, computational life sciences · 2021Article
- A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development.BioMed research international · 2021Review
- Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.European radiology · 2020Article
- Multiomics Analysis and Machine Learning-based Identification of Molecular Signatures for Diagnostic Classification in Liver Disease Types Along the Microbiota-gut-liver Axis.Journal of clinical and experimental hepatologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not perceptible at its initial stages. There are a proportion of features that intensify the threat of emerging cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Ascertaining those features and constructing a classification model to categorize, if the cases are cervical cancer or not is an existing challenging research. This learning intentions at using cervical cancer risk features to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Utmost medical data sets are frequently imbalanced since the number of patients is considerably fewer than the number of non-patients. For the imbalance of the used data set, SMOTE is cast-off to solve this problem. The data set comprises of 32 risk factors and four objective variables: Hinselmann, Schiller, Cytology and Biopsy. Accuracy, Sensitivity, Specificity, PPA and NPA of the four variables remains accurate after SMOTE when compared with values obtained before SMOTE. An RSOnto ontology has been created to visualize the progress in classification performance.
Indexed as
Identifiers
What OpenQuestion holds
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.