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<p>Lung cancer remains the leading cause of cancer-related mortality worldwide, with poor survival rates due to late-stage detection. Current low-dose computed tomography screening faces barriers including high costs and false-positive rates reaching 24%, while artificial intelligence offers opportunities to enhance early detection through longitudinal clinical data analysis. This study developed a Multi-Channel Convolutional Neural Network (MCNN) for lung cancer risk prediction using Taiwan’s National Health Insurance Research Database, encompassing 523,539 patients (2,809 lung cancer, 23,783 other cancer, and 496,947 non-cancer). The MCNN was designed as a lightweight model processing nine channels of diagnostic codes, medications, and medical orders over a three-year observation period. Systematic feature selection reduced estimated feature storage requirements by 99.8%, from approximately 1,184 GB for the full ICD feature space to approximately 2.11 GB for the selected features, while retaining clinical relevance. Model performance was assessed using stratified 10-fold cross-validation against seven machine learning baselines, and interpretability was examined through SHAP analysis. The MCNN achieved an F₁-score of 66.91%, precision of 84.47%, and recall of 59.79%. Ablation studies confirmed multi-modal integration benefits, with diagnostic codes providing primary predictive power. SHAP analysis revealed distinct temporal patterns validating the model’s ability to identify pre-diagnostic phases through healthcare engagement patterns. Findings are based on internal validation within a single national database, and key risk factors such as smoking history are not captured in administrative claims data; future evaluation in independent external cohorts is therefore warranted to confirm these findings. The model’s high precision minimizes false-positive rates while its computational efficiency and clinical interpretability support practical implementation as a complementary claims-based screening support tool for early cancer detection.</p>

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An exploratory study of multi-channel CNN for early detection of lung cancer from longitudinal healthcare records

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Chia-Hui Chien,
  • Shih-Chuan Chang,
  • Yung-Chun Chang,
  • Yu-Chuan Li

摘要

Lung cancer remains the leading cause of cancer-related mortality worldwide, with poor survival rates due to late-stage detection. Current low-dose computed tomography screening faces barriers including high costs and false-positive rates reaching 24%, while artificial intelligence offers opportunities to enhance early detection through longitudinal clinical data analysis. This study developed a Multi-Channel Convolutional Neural Network (MCNN) for lung cancer risk prediction using Taiwan’s National Health Insurance Research Database, encompassing 523,539 patients (2,809 lung cancer, 23,783 other cancer, and 496,947 non-cancer). The MCNN was designed as a lightweight model processing nine channels of diagnostic codes, medications, and medical orders over a three-year observation period. Systematic feature selection reduced estimated feature storage requirements by 99.8%, from approximately 1,184 GB for the full ICD feature space to approximately 2.11 GB for the selected features, while retaining clinical relevance. Model performance was assessed using stratified 10-fold cross-validation against seven machine learning baselines, and interpretability was examined through SHAP analysis. The MCNN achieved an F₁-score of 66.91%, precision of 84.47%, and recall of 59.79%. Ablation studies confirmed multi-modal integration benefits, with diagnostic codes providing primary predictive power. SHAP analysis revealed distinct temporal patterns validating the model’s ability to identify pre-diagnostic phases through healthcare engagement patterns. Findings are based on internal validation within a single national database, and key risk factors such as smoking history are not captured in administrative claims data; future evaluation in independent external cohorts is therefore warranted to confirm these findings. The model’s high precision minimizes false-positive rates while its computational efficiency and clinical interpretability support practical implementation as a complementary claims-based screening support tool for early cancer detection.