Lung cancer is a leading cause of cancer-related deaths, often due to late diagnosis. This study compares various machine learning algorithms—such as Decision Trees, Random Forest, SVM, and Neural Networks—for lung cancer detection. Using clinical and imaging data, models are evaluated based on accuracy, precision, recall, and F1 score. The analysis helps identify the most effective approach for early detection, aiding in timely diagnosis and improved patient outcomes.
Keywords: Cancer detection, medical imaging, Classification algorithms
Tools: Python, Colab, kaggle, data preprocessing, feature selection, performance evaluation metrics
Department: Department of Mathematics
Tools: Python, Colab, kaggle, data preprocessing, feature selection, performance evaluation metrics
Department: Department of Mathematics
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