ArticleIn vivo (Athens, Greece)
Waikato Environment for Knowledge Analysis (WEKA) as a Data Analysis Method Identifying Potential Hematological Parameters for Early Diagnosis of Cervical Cancer.
Article in In vivo (Athens, Greece). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Case Report: PRaG 3.0 therapy for advanced cervical cancer with severe comorbidities.Frontiers in oncology · 2026Article
- Anemia and hematocrit decline in cervical cancer: unveiling clinical consequences and management strategies.Annals of medicine and surgery (2012) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimThe present study explored the use of Waikato Environment for Knowledge Analysis (WEKA) to analyze hematological parameters for distinguishing potential development and progression of cervical cancer. Specifically, we aimed to identify significant biomarkers capable of differentiating atypical squamous cells of undetermined significance (ASC-US) and low-grade squamous intraepithelial lesions (LSIL) from cervical cancer-negative and advanced conditions such as cervical adenocarcinoma. MATERIALS AND
methodsHematological and biochemical data were collected from patients and analyzed using data-mining algorithms available in WEKA. The random forest algorithm was employed to identify patterns among key hematological and biochemical biomarkers, alongside one-way analysis of variance to determine significant alterations in these parameters across cancer-negative, ASC-US, LSIL and adenocarcinoma groups.
resultsRandom forest was the classifier model that demonstrated superior performance metrics with high recall (1.000) and accuracy (0.843), Matthews correlation coefficient (0.510) and area under the curve (0.708), effectively identifying significant patterns within the datasets. One-way analysis of variance indicated significant alterations in red and white blood cell counts, platelet count, hemoglobin, hematocrit and other white blood cell parameters between cancer-negative, ASC-US, LSIL and adenocarcinoma, emphasizing the role of hematological parameters in identifying progression risk.
conclusionThe consistency in conclusions drawn from data mining and statistical analyses highlight the utility of hematological parameters as potential non-invasive biomarkers for cervical cancer screening and progression monitoring. These findings suggest that integrating machine-learning algorithms, particularly random forest, with hematological analysis might enhance early diagnosis and improve clinical outcomes for patients with cervical abnormalities.
Indexed as
Identifiers
What Socratic 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 Socratic graph.