Development and Validation of a Culturally Tailored mHealth-Based Risk Assessment Model for Early Cervical Cancer Detection in Bangladeshi Women

Kamruzzaman Mithu; Zakaria; Sheikh Fahad; Arindam Khaled; Khondaker Abdullah Al

doi:10.25163/data.6110758

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Volume 6 Number 1 2025

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Development and Validation of a Culturally Tailored mHealth-Based Risk Assessment Model for Early Cervical Cancer Detection in Bangladeshi Women

Kamruzzaman Mithu ¹*, Zakaria ¹, Sheikh Fahad ², Arindam Khaled ³, Khondaker Abdullah Al Mamun¹

+ Author Affiliations

Data Modeling 6 (1) 1-8 https://doi.org/10.25163/data.6110758

Submitted: 10 June 2025 Revised: 13 August 2025 Published: 14 August 2025

Abstract

Background: Cervical cancer continues to impose a disproportionate health burden on women living in low- and lower-middle-income countries, where access to organized screening programs often remains limited. In Bangladesh, participation in conventional cervical cancer screening is frequently hindered by cultural discomfort, limited healthcare accessibility, and hesitancy toward invasive gynecological procedures. These challenges highlight the need for alternative, community-oriented approaches capable of supporting early risk identification in resource-constrained settings. This study aimed to develop and validate a culturally tailored, questionnaire-based mHealth model for early cervical cancer risk assessment among Bangladeshi women.Methods: A community-based cross-sectional validation study was conducted among 400 women recruited from two geographically distinct regions of Bangladesh in collaboration with Friendship NGO. The proposed model integrated personal risk factors, family history variables, and symptom-based assessment within a structured scoring framework embedded into a mobile health application named CerviCheck. Physician-led clinical evaluation was used as the comparative reference standard. Model performance was assessed using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, logistic regression analysis, and receiver operating characteristic (ROC) curve analysis.Results: The study cohort demonstrated substantial prevalence of known cervical cancer-associated behavioral and reproductive risk factors, including early sexual debut, high parity, and tobacco exposure. The proposed model achieved an overall accuracy of 88.25%, with sensitivity reaching 100% and specificity estimated at 87.5%. Logistic regression analysis further demonstrated strong predictive contribution from symptom-related variables. ROC curve analysis yielded an area under the curve (AUC) of 0.987, indicating excellent discriminatory performance. Internal cross-validation suggested consistent model stability across the study dataset.Conclusion: The findings suggest that culturally adapted mHealth-based questionnaire systems may offer promising supportive tools for preliminary cervical cancer risk stratification in underserved populations. While not intended to replace clinical diagnostics, the proposed framework may improve early awareness, facilitate referral prioritization, and support community-level preventive screening initiatives in low-resource settings.

Keywords: Cervical cancer, mHealth, Risk assessment, Early detection, Bangladesh

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Development and Validation of a Culturally Tailored mHealth-Based Risk Assessment Model for Early Cervical Cancer Detection in Bangladeshi Women

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