Data Modeling
Mathematical and Computational Data Modeling
0
Citations
1.9k
Views
7
Articles
Volume 6 Number 1 2025
RESEARCH ARTICLE (Open Access)
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
Data Modeling 6 (1) 1-8 https://doi.org/10.25163/data.6110758
Submitted: 10 June 2025 Revised: 13 August 2025 Accepted: 14 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
References
Ahmed, M., Kabir, M. M. J., Kabir, M., & Hasan, M. M. (2019). Identification of the risk factors of cervical cancer applying feature selection approaches. In 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) (pp. 201–204). IEEE.
Alam, D., Robinson, H., Kanungo, A., Hossain, M. D., & Hassan, M. (2013). Health systems preparedness for responding to the growing burden of non-communicable disease: A case study of Bangladesh. Health Policy & Health Finance Knowledge Hub, The Nossal Institute for Global Health, The University of Melbourne.
Alam, N. E., Islam, M. S., Rayyan, F., Ifa, H. N., Khabir, M. I. U., Chowdhury, K., & Mohiuddin, A. (2022). Lack of knowledge is the leading key for the growing cervical cancer incidents in Bangladesh: A population-based, cross-sectional study. PLOS Global Public Health, 2(1), e0000149.
Al-Madani, W., Ahmed, A. E., Arabi, H., Al Khodairy, S., Al Mutairi, N., & Jazieh, A. R. (2019). Modelling risk assessment for cervical cancer in symptomatic Saudi women. Saudi Medical Journal, 40(5), 447–454.
Arbyn, M., Weiderpass, E., Bruni, L., de Sanjosé, S., Saraiya, M., Ferlay, J., & Bray, F. (2020). Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. The Lancet Global Health, 8(2), e191–e203.
Asthana, S., Busa, V., & Labani, S. (2020). Oral contraceptives use and risk of cervical cancer—A systematic review and meta-analysis. European Journal of Obstetrics & Gynecology and Reproductive Biology, 247, 163–175.
Bellinger, J. D., Brandt, H. M., Hardin, J. W., Bynum, S. A., Sharpe, P. A., & Jackson, D. (2013). The role of family history of cancer on cervical cancer screening behavior in a population-based survey of women in the southeastern United States. Women’s Health Issues, 23(4), e197–e204.
Brinton, L. A., Reeves, W. C., Brenes, M. M., Herrero, R., de Britton, R. C., Gaitan, E., Tenorio, F., Garcia, M., & Rawls, W. E. (1989). Parity as a risk factor for cervical cancer. American Journal of Epidemiology, 130(3), 486–496.
Chowdhury, A. M. R., Bhuiya, A., Chowdhury, M. E., Rasheed, S., Hussain, Z., & Chen, L. C. (2013). The Bangladesh paradox: Exceptional health achievement despite economic poverty. The Lancet, 382(9906), 1734–1745.
Dasari, S., Wudayagiri, R., & Valluru, L. (2015). Cervical cancer: Biomarkers for diagnosis and treatment. Clinica Chimica Acta, 445, 7–11.
Deo, S., Sharma, J., & Kumar, S. (2022). GLOBOCAN 2020 report on global cancer burden: Challenges and opportunities for surgical oncologists. Annals of Surgical Oncology, 29(11), 6497–6500.
El-Saharty, S., Ahsan, K. Z., Koehlmoos, T. L., & Engelgau, M. M. (2013). Tackling noncommunicable diseases in Bangladesh: Now is the time. World Bank Publications.
Fatlawi, H. K. (2017). Enhanced classification model for cervical cancer dataset based on cost sensitive classifier. International Journal of Computer Techniques, 4(4), 115–120.
Fernandes, K., Cardoso, J. S., & Fernandes, J. (2017). Transfer learning with partial observability applied to cervical cancer screening. In Pattern Recognition and Image Analysis: 8th Iberian Conference, IbPRIA 2017 (pp. 243–250). Springer.
Fujita, M., Tase, T., Kakugawa, Y., Hoshi, S., Nishino, Y., Nagase, S., Ito, K., Niikura, H., Yaegashi, N., & Minami, Y. (2008). Smoking, earlier menarche and low parity as independent risk factors for gynecologic cancers in Japanese: A case-control study. The Tohoku Journal of Experimental Medicine, 216(4), 297–307.
Ghebre, R. G., Grover, S., Xu, M. J., Chuang, L. T., & Simonds, H. (2017). Cervical cancer control in HIV-infected women: Past, present and future. Gynecologic Oncology Reports, 21, 101–108.
Gravitt, P. E., Silver, M. I., Hussey, H. M., Arrossi, S., Huchko, M., Jeronimo, J., Kapambwe, S., Kumar, S., Meza, G., Nervi, L., et al. (2021). Achieving equity in cervical cancer screening in low- and middle-income countries (LMICs): Strengthening health systems using a systems thinking approach. Preventive Medicine, 144, 106322.
Ilyas, Q. M., & Ahmad, M. (2021). An enhanced ensemble diagnosis of cervical cancer: A pursuit of machine intelligence towards sustainable health. IEEE Access, 9, 12374–12388.
Juneja, A., Sehgal, A., Mitra, A., & Pandey, A. (2003). A survey on risk factors associated with cervical cancer. Indian Journal of Cancer, 40(1), 15–22.
K, H., & Vetriselvi, V. (2022). Deep learning based classification of cervical cancer using transfer learning. In 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC) (pp. 134–139).
Kashyap, N., Krishnan, N., Kaur, S., & Ghai, S. (2019). Risk factors of cervical cancer: A case-control study. Asia-Pacific Journal of Oncology Nursing, 6(3), 308–314.
Liu, Z.-C., Liu, W.-D., Liu, Y.-H., Ye, X.-H., & Chen, S.-D. (2015). Multiple sexual partners as a potential independent risk factor for cervical cancer: A meta-analysis of epidemiological studies. Asian Pacific Journal of Cancer Prevention, 16(9), 3893–3900.
Louie, K., De Sanjose, S., Diaz, M., Castellsague, X., Herrero, R., Meijer, C., Shah, K., Franceschi, S., Muñoz, N., & Bosch, F. (2009). Early age at first sexual intercourse and early pregnancy are risk factors for cervical cancer in developing countries. British Journal of Cancer, 100(7), 1191–1197.
Nessa, A., Ara, R., Fatema, P., Nasrin, B., Chowdhury, A., Khan, K. H., Barua, A. R., & Rashid, M. H. U. (2020). Influence of demographic and reproductive factors on cervical pre-cancer and cancer in Bangladesh. Asian Pacific Journal of Cancer Prevention, 21(7), 1883.
Qayum, M. O., Billah, M. M., Akhter, R., & Flora, M. S. (2021). Women’s knowledge, attitude and practice on cervical cancer and its screening in Dhaka, Bangladesh. Asian Pacific Journal of Cancer Prevention, 22(10), 3327.
Rahman, M. M., Opo, F. A., & Asiri, A. M. (2022). Comprehensive studies of different cancer diseases among less-developed countries. 10(3), 424.
Ratul, I. J., Al-Monsur, A., Tabassum, B., Ar-Rafi, A. M., Nishat, M. M., & Faisal, F. (2022). Early risk prediction of cervical cancer: A machine learning approach. In 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1–4). IEEE.
Raychaudhuri, S., & Mandal, S. (2012). Current status of knowledge, attitude and practice (KAP) and screening for cervical cancer in countries at different levels of development. Asian Pacific Journal of Cancer Prevention, 13(9), 4221–4227.
Roura, E., Castellsague, X., Pawlita, M., Travier, N., Waterboer, T., Margall, N., Bosch, F. X., De Sanjosé, S., Dillner, J., Gram, I. T., et al. (2014). Smoking as a major risk factor for cervical cancer and pre-cancer: Results from the EPIC cohort. International Journal of Cancer, 135(2), 453–466.
Samantaray, A., Kaur, T., Singhal, S., & Gandhi, T. K. (2023). Remote assistance in cervical cancer screening using Microsoft HoloLens 2: An augmented-reality based approach. In 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON) (pp. 123–126). IEEE.
World Health Organization. (2016). Human papillomavirus (HPV) and cervical cancer fact sheet. Geneva, Switzerland.
Wu, W., & Zhou, H. (2017). Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access, 5, 25189–25195.
Recommended articles
Socio-Demographic Profiling and Gendered Livelihoods: A Study of Marginal Farmers in a Lamb Production Upscaling Initiative
Plant-Derived Antidiabetic Foods and Medicinal Botanicals in Bangladesh: A Narrative Review with Systematic Evidence Mapping
Demographic Patterns in Thyroid Dysfunction: Age and Gender Associations from a 7-Year Clinical Cohort in Ibb, Yemen
Article metrics
View details
0
Downloads
0
Citations
20
Views
0
Save
Save
0
Citation
Citation
20
View
View
1
Share
Share