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Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data

Howell, David, Buttery, Ross, Badrinath, Padmanabhan, George, Abraham, Hariprasad, Rithvik, Vousden, Ian, George, Tina, Finnis, Cathy (2023) Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data. BJC Reports, 1 (1). ISSN 2731-9377. (doi:10.1038/s44276-023-00019-5) (KAR id:103320)



Lung cancer has the poorest survival due to late diagnosis and there is no universal screening. Hence, early detection is crucial. Our objective was to develop a lung cancer risk prediction tool at a population level.


We used a large place-based linked data set from a local health system in southeast England which contained extensive information covering demographic, socioeconomic, lifestyle, health, and care service utilisation. We exploited the power of Machine Learning to derive risk scores using linear regression modelling. Tens of thousands of model runs were undertaken to identify attributes which predicted the risk of lung cancer.


Initially, 16 attributes were identified. A final combination of seven attributes was chosen based on the number of cancers detected which formed the Kent & Medway lung cancer risk prediction tool. This was then compared with the criteria used in the wider Targeted Lung Health Checks programme. The prediction tool outperformed by detecting 822 cases compared to 581 by the lung check programme currently in operation.


We have demonstrated the useful application of Machine Learning in developing a risk score for lung cancer and discuss its clinical applicability.

Item Type: Article
DOI/Identification number: 10.1038/s44276-023-00019-5
Uncontrolled keywords: machine learning; cancer risk; oncology
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 19 Oct 2023 13:31 UTC
Last Modified: 08 Jan 2024 10:52 UTC
Resource URI: (The current URI for this page, for reference purposes)

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