JMIR Public Health and Surveillance (Mar 2025)

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

  • Zhen Lu,
  • Binhua Dong,
  • Hongning Cai,
  • Tian Tian,
  • Junfeng Wang,
  • Leiwen Fu,
  • Bingyi Wang,
  • Weijie Zhang,
  • Shaomei Lin,
  • Xunyuan Tuo,
  • Juntao Wang,
  • Tianjie Yang,
  • Xinxin Huang,
  • Zheng Zheng,
  • Huifeng Xue,
  • Shuxia Xu,
  • Siyang Liu,
  • Pengming Sun,
  • Huachun Zou

DOI
https://doi.org/10.2196/67840
Journal volume & issue
Vol. 11
pp. e67840 – e67840

Abstract

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Abstract BackgroundCervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. ObjectiveThis study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. MethodsWe explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. ResultsThis study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P ConclusionsThis study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.