A Computational Approach for Incidence Prediction Models of Lung Cancer in Asia Countries

Lung cancer is a critical public health issue, most prevalent in Asia. This study addresses a gap by holistically investigating risk factors across six Asian countries (Japan, Hong Kong, South Korea, Singapore, Taiwan, India), considering social, economic, and environmental influences. Using machine learning models, cross-validation, PSO optimization, and handling multicollinearity with stepwise regression/VIF, prediction models were developed. Primary risk factors in these Asian countries were identified via statistical analysis. Optimal models were found for each country, achieving mean absolute percentage errors below 7%. Best models included Random Forest (Japan), Cubist (Hong Kong), SVR (South Korea/India), and Linear Regression (Singapore/Taiwan). Comparing developed/developing countries revealed risk factor differences and commonalities. These accurate models precisely estimate incidence rates, providing valuable data for tailored lung cancer prevention strategies in Asian countries.

• Publication year

  2025

• Venue

  2025 IEEE 13th Region 10 Humanitarian Technology Conference (R10-HTC)

• Publication date

  2025-09-29

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/R10-HTC63995.2025.11394101
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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