Lung cancer (LC) remains the primary cause of cancer-related mortality,
largely due to late-stage diagnoses. Effective strategies for early detection
are therefore of paramount importance. In recent years, machine learning (ML)
has demonstrated considerable potential in healthcare by facilitating the
detection of various diseases. In this retrospective development and validation
study, we developed an ML model based on dynamic ensemble selection (DES) for
LC detection. The model leverages standard blood sample analysis and smoking
history data from a large population at risk in Denmark. The study includes all
patients examined on suspicion of LC in the Region of Southern Denmark from
2009 to 2018. We validated and compared the predictions by the DES model with
diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had
complete data of which 2,505 (25\%) had LC. The DES model achieved an area
under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%,
specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%,
and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed
all five pulmonologists, achieving a sensitivity 9\% higher than their average.
The model identified smoking status, age, total calcium levels, neutrophil
count, and lactate dehydrogenase as the most important factors for the
detection of LC. The results highlight the successful application of the ML
approach in detecting LC, surpassing pulmonologists' performance. Incorporating
clinical and laboratory data in future risk assessment models can improve
decision-making and facilitate timely referrals.

通过丹麦地区 2009 年至 2018 年的肺癌疑似患者数据，利用动态集成选择的机器学习模型，在标准血样分析和吸烟史数据基础上，成功实现肺癌的早期检测，表现优于肺病学家，关键因素包括吸烟状况、年龄、总钙水平、中性粒细胞计数和乳酸脱氢酶。