--- title: "Ensemble machine learning versus Cox regression to predict colorectal cancer risk in people with H" id: "bmj-open-0-comparison-of-an-ensemble-machine-learning-model-to-a-cox-regression-model-to" canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-0-comparison-of-an-ensemble-machine-learning-model-to-a-cox-regression-model-to" content_type: "clinical_feed_article" specialty: "Infectious Disease" source_name: "BMJ Open" source_url: "http://bmjopen.bmj.com/cgi/content/short/16/9/e108693?rss=1" published_at: "2026-09-09T11:16:01.000Z" evidence_level: "Journal Feed" license: "CC-BY-NC-4.0 / Informational Use" --- # Ensemble machine learning versus Cox regression to predict colorectal cancer risk in people with H ## Provenance & Clinical Metadata - **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-0-comparison-of-an-ensemble-machine-learning-model-to-a-cox-regression-model-to - **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md) - **Primary Source:** BMJ Open - **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/9/e108693?rss=1) - **Published At:** 2026-09-09T11:16:01.000Z - **Evidence Rating:** Journal Feed ## Executive GIST (TL;DR) - There are no screening guidelines specifically for **colorectal cancer (CRC)** in **people with HIV (PWH)**; existing practice has been to apply recommendations for people without HIV (PWoH) despite evidence of higher CRC prevalence and earlier onset among PWH. - The study will develop an **ensemble machine learning (ML)** model, using stacking of multiple ML approaches, to predict CRC risk among PWH from comprehensive nationwide Swedish registers. - Predictive performance of the ensemble ML model will be compared against a baseline **Cox proportional hazards** regression model; the better-performing method will be used to support personalised CRC screening for PWH. - The study cohort includes all PWH and matched PWoH born 1940–2008, aged ≥18, living in Sweden at any time between 1983 and 2024, linked across six national demographic and healthcare registers. - Follow-up continues until first incident CRC, emigration or death; the primary outcome is incident CRC identified from registers. - PWH will be matched to negative controls at a ratio of 1:10; Cox regression will be performed first to provide baseline estimates for comparison with the ensemble ML results. - A variety of ML algorithms will be combined via stacking to form the ensemble; the study emphasises ML suitability for complex or high-dimensional data. - Ethical approval has been granted by the Regional Ethical Committee in Sweden under multiple Dnr numbers; retrospective, pseudonymised register-based design minimises participant risk and results will be reported at population level to prevent identification. - Study findings will be submitted for peer-reviewed publication and the chosen predictive method will inform a CRC risk prediction model intended to personalise screening recommendations for PWH. ## Clinical Analysis & Structured Key Points Introduction There are currently no colorectal cancer (CRC) screening recommendations specifically outlined for people with HIV (PWH). Screening measures used for people without HIV (PWoH) have been previously discussed as sufficient for use among PWH, despite observations of higher CRC prevalence and CRC reportedly appearing at earlier ages among PWH in comparison to PWoH. Machine learning (ML) methods are regarded as robust approaches that may enhance predictive performance, particularly in the context of complex or high-dimensional data. This study aims to develop an ensemble ML model to predict CRC risk in PWH using comprehensive nationwide datasets. The model's predictive performance will be evaluated and compared with a baseline Cox proportional regression model. The better-performing method will be implemented to develop a CRC risk prediction model with the aim of personalising screening recommendations for PWH. Methods and analysis The study population will include all PWH and PWoH born between 1940 and 2008, aged 18 or older and living in Sweden sometime between 1983 and 2024. The study population will be linked to six nationwide demographic and healthcare registers. Follow-up will continue until the first incident of CRC, emigration or death. The outcome of interest is CRC. PWH will be matched to negative controls 1:10. A Cox regression analysis will be completed first, and the results will be used as a baseline comparison to the ensemble ML results. A range of ML methods will be used to develop the ensemble model using stacking. Ethics and dissemination This study has ethical approval from the Regional Ethical Committee in Sweden (Dnr: 2024-04185-02, 2024-06783-02, 2023-00191-01, 2022-02897-02, 2022-05624-01, 2018/11-31/2). Given that the study is retrospective and register-based, using only pseudonymised data, there are minimal physical, psychological or privacy risks to included individuals. All results will be presented at the population level with no possibility of identification. 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