ArticleSensors (Basel, Switzerland)2020
Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital health interventions for cervical cancer care: A systematic review and future research opportunities.PloS one · 2023Pooled it
- Exploratory characterization of gut microbiota and cognitive profiles in adolescents with subthreshold depression: a shotgun metagenomics sequencing study.Npj mental health research · 2026Article
- Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer.Oncology reviews · 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
- Explainable AI based cervical cancer prediction using FSAE feature engineering and H2O AutoML.Scientific reports · 2025Article
- A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.Neurogenetics · 2025Article
- Analyzing the Centers for Disease Control and Prevention Mortality Data Using Weekly Exceedance in Mortality Count and Weekly Change in Mortality Indicator: A Time Series Study.Health science reports · 2025Article
- A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.Scientific reports · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Cervical Cancer Detection Using Deep Neural Network and Hybrid Waterwheel Plant Optimization Algorithm.Bioengineering (Basel, Switzerland) · 2025Article
- Colony Site Selection of Gray Heron (Ecology and evolution · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Article
- Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival.Scientific reports · 2024Article
- Edge computing-based ensemble learning model for health care decision systems.Scientific reports · 2024Article
- Construction and evaluation of a liver cancer risk prediction model based on machine learning.World journal of gastrointestinal oncology · 2024Article
- Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer.Scientific reports · 2024Article
- Predictive models for personalized precision medical intervention in spontaneous regression stages of cervical precancerous lesions.Journal of translational medicine · 2024Article
- Prediction of precancerous cervical cancer lesions among women living with HIV on antiretroviral therapy in Uganda: a comparison of supervised machine learning algorithms.BMC women's health · 2024Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.
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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.