Bambo Ayo Adeyanju

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.

This study presents a leakage‐aware machine learning framework for predicting minimum horizontal stress ( σ _h min) using structured geomechanical and fracture‐related parameters. A dataset comprising 21,499 records from approximately 200 horizontal wells in the Marcellus Shale was preprocessed using a strictly leakage‐controlled pipeline, including feature refinement, outlier capping, and training‐fold–based transformations. Three ensemble models—random forest (RF), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost)—along with a stacking regressor and artificial neural network (ANN), were evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination ( R ² ). The RF model achieved the best baseline performance (RMSE = 6.5846, R ² = 0.9988), while gradient boosting showed improved performance after tuning (RMSE ≈ 6.59). The stacking model delivered competitive results (RMSE = 7.4963), whereas the ANN showed lower performance (RMSE = 55.5008), indicating limited suitability for structured tabular data. A scenario‐based evaluation using Nigerian reservoir data demonstrated reasonable pr3332edictive consistency but was not treated as external validation due to data limitations. The results confirm that leakage‐aware ensemble learning provides a robust and physically consistent approach for predicting minimum horizontal stress, with potential application in reservoir characterization and hydraulic fracturing design.