Diagnosing Cervical Cancer Using Machine Learning Methods
Author:
Affiliation:
1. Institute of Graduate Studies, Altinbaş University,İstanbul,Turkey
2. AltinbaşUniversity,Software Engineering,İstanbul,Turkey
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9799780/9799803/09800033.pdf?arnumber=9800033
Reference15 articles.
1. Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier
2. Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods
3. Machine learning for assisting cervical cancer diagnosis: An ensemble approach
4. Optimization of Decision Tree Machine Learning Strategy in Data Analysis
5. Random forest (machine learning)
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1. Efficient Feature Selection Approach for Prediction of Cervical Cancer Using Machine Learning Algorithms;2023 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom);2023-12-07
2. An Efficient Deep Learning Model for Intraoperative Tissue Classification in Gynecological Cancer;2023 9th International Conference on Smart Structures and Systems (ICSSS);2023-11-23
3. Early diagnosis of cervical cancer using Machine Learning approaches: Comparative Analysis;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01
4. Expert System Techniques in Intelligent Diagnostic Digital Cytopathology System for Cervical Intraepithelial Neoplasia Detection;2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT);2023-02-11
5. A Novel and Effective method for Early Identification of Cervical Cancer based on Gradient Boosting Classifier;2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF);2023-01-05
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