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Lung cancer is a hazardous disease in today’s mankind. This is the most common disease to be found in men and second most common disease to be found in women. The major risks that may affect lung cancer are nicotine usage, HIV infections (only in developed countries), radioactive gasses and family history. Due to the rapidly increasing incidence, there is a high time they develop more sophisticated machine learning algorithms to predict lung cancer risk. This study focuses on predicting the lung cancer risk of an individual. The four different ML models used in this study are the DT classifier, KNN classifier, RF classifier and SV classifier. The SVC manages to reach the highest precision registered during experiments (97%) confirming that is a better model than the other four models.

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Feature-Based Machine Learning Models Implementation for Lung Cancer Classification and Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Gagan Sharma,
  • Baibaswata Mohapatra,
  • Merry Saxena,
  • Atul Tiwary,
  • Pradeepta Kumar Sarangi,
  • Srikanta Kumar Mohapatra

摘要

Lung cancer is a hazardous disease in today’s mankind. This is the most common disease to be found in men and second most common disease to be found in women. The major risks that may affect lung cancer are nicotine usage, HIV infections (only in developed countries), radioactive gasses and family history. Due to the rapidly increasing incidence, there is a high time they develop more sophisticated machine learning algorithms to predict lung cancer risk. This study focuses on predicting the lung cancer risk of an individual. The four different ML models used in this study are the DT classifier, KNN classifier, RF classifier and SV classifier. The SVC manages to reach the highest precision registered during experiments (97%) confirming that is a better model than the other four models.