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Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine.

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Environmental and lifestyle factors significantly influence lung cancer mortality. This study identifies country-specific human behavior indicators and uses Support Vector Machine (SVM) for accurate lung cancer prediction.

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Area of Science:

  • Oncology
  • Environmental Health
  • Public Health

Background:

  • Lung cancer remains a leading global cause of death.
  • Environmental and lifestyle factors are primary drivers of most sporadic cancers.
  • Predicting cancer mortality using human behavior indicators is challenging due to limited sample sizes.

Purpose of the Study:

  • To identify and weigh 30 human-behavior indicators across seven categories influencing lung cancer.
  • To assess the predictive power of these indicators for lung cancer mortality.
  • To determine if indicator importance is country-specific.

Main Methods:

  • Data extracted from OECD and WHO databases for 13 countries (1998-2013).
  • Utilized Support Vector Machine (SVM) to analyze 30 human-behavior indicators.
  • Examined indicator weights and predictive accuracy for lung cancer mortality (2014-2016).

Main Results:

  • Human-behavior indicators influencing lung cancer are country-specific.
  • Support Vector Machine (SVM) demonstrated strong predictive capabilities.
  • Achieved an average prediction accuracy of 96.08% for lung cancer mortality across 13 countries.

Conclusions:

  • Country-specific human behavior analysis is crucial for understanding lung cancer drivers.
  • SVM is a powerful tool for predicting lung cancer mortality.
  • Environmental and lifestyle interventions should be tailored to country-specific risk factors.