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Introduction <p>Cervical cancer is a malignancy associated with human immunodeficiency virus, characterized by abnormal cervical cell mutations. Machine learning techniques offer valuable support for early detection and prediction of cervical cancer, potentially lowering screening and treatment costs. This study specifically targeted women living with human immunodeficiency virus, aiming to identify the most significant predictors of cervical cancer and to determine the most effective supervised machine learning model for its prediction within this population.</p> Methods <p>This study employed a multi-center, cross-sectional design using a secondary dataset from the smart care systems of four antiretroviral therapy clinics in central Debre Markos town. To determine the most relevant predictors, seven machine learning models, Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and AdaBoost were implemented to identify the top-performing model. Model performance was assessed using the confusion matrix and the Area under the Receiver Operating Characteristic Curve.</p> Results <p>The findings indicated that adherence at enrollment, screening visit type, and nutritional status, months on anti-retroviral therapy, follow-up status, and weight were highly important predictors of cervical cancer. Among the evaluated models, the K-Nearest Neighbors model outperformed the others, achieving the highest accuracy of 98% and an Area under the Receiver Operating Characteristic Curve of 0.68.</p> Conclusion <p>As demonstrated in this study, the K-Nearest Neighbors model showed the best performance in effectively predicting cervical cancer among women living with human immunodeficiency virus. Strengthening nutritional support interventions, improving follow-up mechanisms, and enhancing anti-retroviral therapy adherence counseling programs may collectively contribute to reducing the risk of cervical cancer among women living with human immunodeficiency virus. Future research should focus on validating the predictive model across diverse geographic regions and healthcare contexts to enhance its generalizability, robustness, and practical applicability.</p>

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Predicting cervical cancer among women living with HIV/AIDS at public health facilities in a resource-limited setting in Ethiopia using machine learning analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Muluken Belachew Mengistie,
  • Simane Kefale Mengstie,
  • Gizaw Hailiye Teferi,
  • Aynadis Worku Shume,
  • Wubete Lule Ayalew,
  • Mezigebu Lule Ayalew,
  • Abraham Keffale Mengistu,
  • Sefefe Birhanu Tizie,
  • Ayenew Sisay Gebeyew,
  • Misgie Kassanew Terefe,
  • Lijalem Megibaru Enyew

摘要

Introduction

Cervical cancer is a malignancy associated with human immunodeficiency virus, characterized by abnormal cervical cell mutations. Machine learning techniques offer valuable support for early detection and prediction of cervical cancer, potentially lowering screening and treatment costs. This study specifically targeted women living with human immunodeficiency virus, aiming to identify the most significant predictors of cervical cancer and to determine the most effective supervised machine learning model for its prediction within this population.

Methods

This study employed a multi-center, cross-sectional design using a secondary dataset from the smart care systems of four antiretroviral therapy clinics in central Debre Markos town. To determine the most relevant predictors, seven machine learning models, Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Extreme Gradient Boosting, and AdaBoost were implemented to identify the top-performing model. Model performance was assessed using the confusion matrix and the Area under the Receiver Operating Characteristic Curve.

Results

The findings indicated that adherence at enrollment, screening visit type, and nutritional status, months on anti-retroviral therapy, follow-up status, and weight were highly important predictors of cervical cancer. Among the evaluated models, the K-Nearest Neighbors model outperformed the others, achieving the highest accuracy of 98% and an Area under the Receiver Operating Characteristic Curve of 0.68.

Conclusion

As demonstrated in this study, the K-Nearest Neighbors model showed the best performance in effectively predicting cervical cancer among women living with human immunodeficiency virus. Strengthening nutritional support interventions, improving follow-up mechanisms, and enhancing anti-retroviral therapy adherence counseling programs may collectively contribute to reducing the risk of cervical cancer among women living with human immunodeficiency virus. Future research should focus on validating the predictive model across diverse geographic regions and healthcare contexts to enhance its generalizability, robustness, and practical applicability.