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In recent years, machine learning has shown significant promise in the treatment of lung cancer disease, which ranks among the deadliest diseases in the world. The central objective of this analysis is to find the initial phases of lung cancer and assess the precision of diverse ML algorithms. Detecting cancer remains a tough task for medical professionals. We still don’t fully understand what causes cancer, and we haven't discovered a complete cure for it. The prediction of lung cancer is done with different ML methods like Decision Tree, Gaussian Naive Bayes, SVC, LR, KNN, and Random Forest. After reviewing various research papers, we discovered that certain classifiers show lower accuracy, while others, although more accurate, still struggle to reach close to 100%. Our findings suggest that the Logistic Regression (LR) model consistently produces more accurate results when predicting lung cancer.

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Lung Cancer Prediction Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Rambabu Pemula,
  • A. Obulesh,
  • Tarange Anjali,
  • Yadla Charanmai,
  • Ch. Ravi Kishore

摘要

In recent years, machine learning has shown significant promise in the treatment of lung cancer disease, which ranks among the deadliest diseases in the world. The central objective of this analysis is to find the initial phases of lung cancer and assess the precision of diverse ML algorithms. Detecting cancer remains a tough task for medical professionals. We still don’t fully understand what causes cancer, and we haven't discovered a complete cure for it. The prediction of lung cancer is done with different ML methods like Decision Tree, Gaussian Naive Bayes, SVC, LR, KNN, and Random Forest. After reviewing various research papers, we discovered that certain classifiers show lower accuracy, while others, although more accurate, still struggle to reach close to 100%. Our findings suggest that the Logistic Regression (LR) model consistently produces more accurate results when predicting lung cancer.