Article: Intelligence ensemble feature selection and ensemble classifier for cervical cancer diagnosis Journal: International Journal of Intelligent Engineering Informatics (IJIEI) 2025 Vol.13 No.4 pp.432 - 460 Abstract: Cervical cancer is one of the most common diseases affecting women, which may be prevented with an early diagnosis. Numerous recent studies using DL and ML techniques have focused on early-stage prediction of this disease. DL methods, it's critical to understand the importance of factors for appropriately classifying patients with cervical cancer. In other words, FS selects the most important features from the dataset when building a DL model and introduces IEFS and DLE for cervical cancer. IEFS has been introduced to choose the important features and remove redundant features to train a model using the DLE classifier. The IEFS model combines the results of methods like EBFO, EEHO, and KLBWO, creating an optimal best subset. An aggregation function has combined the results of individual methods. DLE is a DL approach that combines several methods (GAN, BGRU, and DWCNN) in cancer diagnosis. The LogitBoost model combines the output from different classifiers. UCI is used to collect cervical cancer (risk factors) to measure the results of the proposed classifier against current classifiers. MATLABR2020a has been used to simulate and measure the classifiers using precision, recall/sensitivity, F-measure, specificity, and accuracy metrics. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligence ensemble feature selection and ensemble classifier for cervical cancer diagnosis

Authors: Anjali Kuruvilla; B. Jayanthi

Addresses: School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, Coimbatore, Tamil Nadu, India ' School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, Coimbatore, Tamil Nadu, India

Abstract: Cervical cancer is one of the most common diseases affecting women, which may be prevented with an early diagnosis. Numerous recent studies using DL and ML techniques have focused on early-stage prediction of this disease. DL methods, it's critical to understand the importance of factors for appropriately classifying patients with cervical cancer. In other words, FS selects the most important features from the dataset when building a DL model and introduces IEFS and DLE for cervical cancer. IEFS has been introduced to choose the important features and remove redundant features to train a model using the DLE classifier. The IEFS model combines the results of methods like EBFO, EEHO, and KLBWO, creating an optimal best subset. An aggregation function has combined the results of individual methods. DLE is a DL approach that combines several methods (GAN, BGRU, and DWCNN) in cancer diagnosis. The LogitBoost model combines the output from different classifiers. UCI is used to collect cervical cancer (risk factors) to measure the results of the proposed classifier against current classifiers. MATLABR2020a has been used to simulate and measure the classifiers using precision, recall/sensitivity, F-measure, specificity, and accuracy metrics.

Keywords: deep learning ensemble; DLE; University of California Irvine; UCI; deep learning; DL; machine learning; ML; dynamic weight convolutional neural network; DWCNN; generative adversarial network; GAN; feature selection; FS.

DOI: 10.1504/IJIEI.2025.150103

International Journal of Intelligent Engineering Informatics, 2025 Vol.13 No.4, pp.432 - 460

Received: 16 Apr 2024
Accepted: 25 Aug 2024
Published online: 01 Dec 2025 *

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Title: Intelligence ensemble feature selection and ensemble classifier for cervical cancer diagnosis

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