Cancer Guard: A Machine Learning Approach for Early Detection and Prediction of Lung Cancer

The present paper explores a machine learning strategy for early detection and diagnosis of lung cancer. The primary objective of this research is to identify the most effective machine learning algorithm and predictive attributes for accurately detecting early-stage lung cancer. The study encompasses a range of predictive factors, including fatigue, alcohol consumption, allergies, yellow fingers, and others. Machine learning techniques, including binary logistic regression, were employees to analyze the data and predict the most precise outcomes Cancer of the lung’s discovery. The findings of this research reveal that the binary logistic regression machine learning model achieves an amazing precision rate of 94.8% in improving lung cancer detection and diagnosis. These results hold great promise for healthcare professionals, as they can utilize the insights gains from this study to enhance patient outcomes by employing the most reliable predictive characteristics AI-based technologies for lung disease identification. Overall, this research offers valuable insights into the factors associated with lung cancer and provides a strong foundation for further investigation and development in this critical area of study.