A Machine Learning and Bayesian Belief Network Approach to Predicting Cervical Cancer Risk - Implications for Risk Management

Aug 2, 2026

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A machine learning and Bayesian Belief Network framework predicts cervical cancer risk to strengthen risk stratification and early detection. The study analyzes data from 858 patients, addressing missing values, class imbalance, and nonlinear feature interactions through multiple imputation, feature selection, and imbalance mitigation. Results show high performance across cervical screening tests, with a combined target ML model reaching 95.6% accuracy and AUROC 0.958, while the BBN model reports sensitivity 91.3% and specificity 86.8%. Findings support integration into clinical decision support and highlight interdisciplinary collaboration for safer, more effective AI healthcare.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Cervical cancer burden and screening limitations

Methods

Data cohort and preprocessing challenges

Modeling approach: ML and BBN

Results

Performance across screening tests

Discussion

Clinical integration and implications for risk management

Journal of Multidisciplinary Healthcare downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Journal of Multidisciplinary Healthcare Open Access Full Text Article ORIGINAL RESEARCH A Machine Learning and Bayesian Belief Network Approach to Predicting Cervical Cancer Risk: Implications for Risk Management Khaled Toffaha 1 , Mecit Can Emre Simsekler 1 , Andrei Sleptchenko 1 , Michael A Kortt 1 , Laurette L Bukasa2 1Department of Management Science & Engineering, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates; 2Abu Dhabi Health Data Services, M42, Abu Dhabi, United Arab Emirates Correspondence: Khaled Toffaha; Mecit Can Emre Simsekler, Department of Management Science & Engineering, Khalifa University of Science & Technology, Abu Dhabi, United Arab Emirates, Email [Khaled.mToffaha@ku.ac.ae](Khaled.mToffaha@ku.ac.ae); [Emre.Simsekler@ku.ac.ae](Emre.Simsekler@ku.ac.ae) | Introduction: Cervical cancer remains a major global health challenge, necessitating enhanced risk stratification and early detection methodologies. This study proposes a comprehensive predictive framework for cervical cancer leveraging advanced machine learning (ML) algorithms and Bayesian Belief Networks (BBNs), illustrating the transformative role of digital technologies in healthcare and education within an increasingly digitized society.
Methods: A cohort of 858 patients was analyzed, addressing data challenges, including missing values, class imbalance, and nonlinear feature interactions, that frequently compromise the reliability of predictive modeling. Methodologically, this study integrated advanced data science approaches, including multiple imputation, feature selection, and imbalance mitigation, advancing medical analytics to ensure robust model generalizability.
Results: High predictive performance was observed across different cervical cancer screening tests. The combined target ML model achieved an accuracy of 95.6%, an area under the receiver-operating characteristic curve (AUROC) of 0.958, and an F1-score of 0.945. The BBN, built upon the Bayesian Additive Regression Trees (BART) model, demonstrated a positive prediction rate (sensitivity) of 91.3% and a negative prediction rate (specificity) of 86.8% .
Discussion: These results validate the technical efficacy of the proposed framework and underscore its potential for integration into clinical decision-support systems. Beyond clinical applications, this research contributes to computational oncology by demonstrating the synergistic potential of combining probabilistic graphical models with ML techniques. The study highlights the critical role of interdisciplinary collaboration between clinical experts and data scientists in creating effective AI healthcare solutions. It also emphasizes the need for upskilling healthcare workers and optimizing healthcare delivery processes to fully realize the benefits of precision medicine.
Keywords: risk management, cervical cancer risk prediction, future of healthcare, cancer risk factors, Bayesian belief network, machine learning, digital health, patient safety | | --- | | Introduction
Cervical cancer continues to represent a major global public health concern, particularly in low- and middle-income countries (LMICs), where approximately 90% of cervical cancer deaths occur despite the availability of effective screening and vaccination programs in high-income nations.1 It is estimated that more than 600,000 new cases of cervical cancer are diagnosed each year worldwide, leading to more than 340,000 deaths.2 The disparity in outcomes is mainly attributable to inequalities in access to preventive healthcare services, timely diagnosis, and treatment availability. In the United States alone, about 13,820 new cases of invasive cervical cancer were expected to be diagnosed in 2024, with an estimated 4360 women projected to die from the disease.3 In addition to the h

FAQs

What modeling approach does the study use to predict cervical cancer risk?

The research combines advanced machine learning algorithms with a Bayesian Belief Network (BBN) constructed upon a Bayesian Additive Regression Trees (BART) model.

How does the study handle common data issues that affect predictive modeling?

It addresses missing values, class imbalance, and nonlinear feature interactions using multiple imputation, feature selection, and imbalance mitigation.

What performance metrics were reported for the proposed framework?

The combined target ML model achieved 95.6% accuracy and AUROC 0.958 with an F1-score of 0.945. The BBN model showed sensitivity (positive prediction rate) of 91.3% and specificity (negative prediction rate) of 86.8%.

Aug 2, 2026

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