Background: Lung cancer is a leading cause of cancer-related mortality worldwide, often diagnosed in advanced stages, making early detection critical. This study aimed to evaluate the performance of various machine learning models in predicting lung cancer risk based on epidemiological questionnaires, comparing them with traditional logistic regression models. Methods: A retrospective case–control study was conducted using data from 5421 lung cancer cases and 10,831 matched controls. The dataset included a wide range of demographic, clinical, and behavioral risk factors from epidemiological questionnaires. We developed and compared multiple machine learning algorithms, including LightGBM and stacking ensemble models, alongside logistic regression for predicting lung cancer risk. Model performance was evaluated using accuracy, area under the curve (AUC), and recall. Results: The stacking model outperformed traditional logistic regression, achieving an AUC of 0.887 (0.870–0.903) compared to 0.858 (0.839–0.878) for logistic regression. LightGBM also performed well, with an AUC of 0.884 (0.867–0.901). The stacking model achieved an accuracy of 81.2%, with a recall of 0.755, higher than the logistic regression model’s accuracy of 79.4%. Compared to classical lung cancer prediction models (LLP and PLCO), the logistic regression and ML models improved AUC by 12% to 27%. Conclusions: Integrating machine learning models into lung cancer screening programs can significantly enhance early detection efforts. Machine learning approaches, such as LightGBM and stacking, offer improved accuracy and predictive power over traditional models. However, efforts to enhance model interpretability through explainable AI techniques are necessary for broader clinical adoption.
背景：肺癌是全球癌症相关死亡的主要原因，通常诊断时已处于晚期，因此早期检测至关重要。本研究旨在评估基于流行病学问卷的多种机器学习模型预测肺癌风险的表现，并将其与传统逻辑回归模型进行比较。方法：采用回顾性病例对照研究设计，纳入5421例肺癌患者和10831例匹配对照者的数据。数据集包含流行病学问卷中广泛的人口统计学、临床和行为风险因素。我们开发并比较了多种机器学习算法（包括LightGBM和堆叠集成模型）与逻辑回归模型在肺癌风险预测中的表现，使用准确率、曲线下面积（AUC）和召回率评估模型性能。结果：堆叠模型表现优于传统逻辑回归模型，其AUC为0.887（0.870–0.903），而逻辑回归模型的AUC为0.858（0.839–0.878）。LightGBM模型同样表现良好，AUC达到0.884（0.867–0.901）。堆叠模型的准确率为81.2%，召回率为0.755，高于逻辑回归模型79.4%的准确率。与经典肺癌预测模型（LLP和PLCO）相比，逻辑回归和机器学习模型将AUC提升了12%至27%。结论：将机器学习模型整合到肺癌筛查项目中可显著提升早期检测效果。LightGBM和堆叠等机器学习方法相较于传统模型具有更高的准确率和预测能力。然而，需要通过可解释人工智能技术提升模型可解释性，以促进更广泛的临床应用。
肿瘤(癌症)患者之家