High-Accuracy Machine Learning Models for Cervical Cancer Prediction Using Ensemble Techniques and Class Imbalance Correction

DOI

書誌事項

公開日
2026-01-29
バージョン
01
DOI
  • 10.5954/icarob.2026.os4-7
公開者
株式会社ALife Robotics

説明

This study develops a machine learning-based predictive model for cervical cancer detection using a dataset of 835 patient records encompassing 36 clinical and demographic features. A systematic methodology was employed, including mean imputation for missing data, StandardScaler for feature normalisation, and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Multiple algorithms, Decision Tree, Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and K-Nearest Neighbours, were trained and evaluated using an 80/20 train-test split. The results confirm the feasibility of using ensemble-based models as reliable clinical decision-support tools for early cancer screening.

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キーワード

詳細情報 詳細情報について

  • CRID
    1390308043759244544
  • DOI
    10.5954/icarob.2026.os4-7
  • ISSN
    21887829
  • 本文言語コード
    en
  • データソース種別
    • JaLC
  • 抄録ライセンスフラグ
    使用不可

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