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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Oluwatobi Akinlade
Birmingham City University
Computer Science & IT Engineering

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Ensuring effective cervical cancer diseases diagnosis system using ensemble machine learning model Oluwatobi Akinlade; Jafar Abdollahi; Misan Paul Etchie; Sunday Adeola Ajagbe; Oluwaseyi Omotayo Alabi; Bambo Ayo Adeyanju
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2410-2422

Abstract

Worldwide, cervical cancer is a major public health concern, and early detection is essential for successful management and treatment. Ensemble machine learning (ML) has emerged as a promising approach for improving the accuracy and reliability of cervical cancer diagnosis systems. This study evaluated the performance of various ensemble ML models for cervical cancer diagnosis using a large dataset of cervical cell images. The performance of different ensemble models was compared, including random forest (RF), gradient boosting (GB), and stacking, with conventional ML models, such as logistic regression and support vector machine (SVM). The bagging ensemble model developed reported training of 0.991667, 0.827586, 1.0, and 0.90566 for accuracy, precision, recall, and F-measure (F1-score), respectively. Furthermore, the interpretability of ensemble models was investigated using feature importance and partial dependence plots. The interpretability analysis revealed that ensemble models can provide valuable insights into the key features and factors that contribute to cervical cancer diagnosis. In conclusion, the findings suggest that ensemble ML is a promising approach for developing accurate and reliable cervical cancer diagnosis systems and improving the interpretability of these models.
Co-Authors Bambo Ayo Adeyanju Jafar Abdollahi Misan Paul Etchie Oluwaseyi Omotayo Alabi Sunday Adeola Ajagbe