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Background <p>Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitate early intervention and prevention of lung cancer.</p> Methods <p>We stratified the population into six subgroups according to age and gender. For each subgroup, random forest, extreme gradient boosting, deep neural networks, support vector machine, multiple logistic regression and deep Q network (DQN) models were developed and validated. Models were trained and tested using samples from 2000 to 2015 and independent external validated through those from 2016 to 2019. The suitable model for lung cancer risk prediction and high risk factors identification was chosen based on internal validation and independent external validation.</p> Results <p>The DQN model achieved the optimal prediction performance in stratified subgroups, with AUROC ranging from 0.937 to 0.953, recall ranging from 0.932 to 0.943, <i>F</i><sub><i>2</i></sub>-score ranging from 0.929 to 0.946, precision ranging from 0.926 to 0.952, <i>F</i><sub><i>1</i></sub>-score ranging from 0.933 to 0.963 and RMSE ranging from 0.21 to 0.27. SHAP values were supplied for model interpretability. High risk factors of lung cancer incidence were identified in the elderly. Men ≥ 65 carrying C &gt; A/G &gt; T mutation had the highest lung cancer incidence decrease of 39.5% after five years quitting in stratified elderly groups, which were 1.83 times more than women ≥ 65 not carrying C &gt; A/G &gt; T mutation.</p> Conclusions <p>The DQN model may be suitable for identifying high risk factors and predicting lung cancer risk with high performance. The proposed intervention and diagnosis pathways could be used for early screening and intervention before the occurrence of lung cancer, which could help oncologists develop targeted intervention strategies for the stratified elderly to reduce lung cancer incidence and improve therapeutic effect. Proposed method could also be used in predicting the risk of other chronic diseases to help conduct intervention and reduce incidence.</p>

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Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月22日

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  • Songjing Chen,
  • Sizhu Wu

摘要

Background

Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitate early intervention and prevention of lung cancer.

Methods

We stratified the population into six subgroups according to age and gender. For each subgroup, random forest, extreme gradient boosting, deep neural networks, support vector machine, multiple logistic regression and deep Q network (DQN) models were developed and validated. Models were trained and tested using samples from 2000 to 2015 and independent external validated through those from 2016 to 2019. The suitable model for lung cancer risk prediction and high risk factors identification was chosen based on internal validation and independent external validation.

Results

The DQN model achieved the optimal prediction performance in stratified subgroups, with AUROC ranging from 0.937 to 0.953, recall ranging from 0.932 to 0.943, F2-score ranging from 0.929 to 0.946, precision ranging from 0.926 to 0.952, F1-score ranging from 0.933 to 0.963 and RMSE ranging from 0.21 to 0.27. SHAP values were supplied for model interpretability. High risk factors of lung cancer incidence were identified in the elderly. Men ≥ 65 carrying C > A/G > T mutation had the highest lung cancer incidence decrease of 39.5% after five years quitting in stratified elderly groups, which were 1.83 times more than women ≥ 65 not carrying C > A/G > T mutation.

Conclusions

The DQN model may be suitable for identifying high risk factors and predicting lung cancer risk with high performance. The proposed intervention and diagnosis pathways could be used for early screening and intervention before the occurrence of lung cancer, which could help oncologists develop targeted intervention strategies for the stratified elderly to reduce lung cancer incidence and improve therapeutic effect. Proposed method could also be used in predicting the risk of other chronic diseases to help conduct intervention and reduce incidence.