Download PDFOpen PDF in browserMachine Learning Algorithms for Lung Cancer Detection: Application of Different Machine Learning Algorithms for Lung Cancer Detection.EasyChair Preprint 1279518 pages•Date: March 27, 2024AbstractMachine learning algorithms have emerged as powerful tools for lung cancer detection, offering the potential to improve accuracy and efficiency in diagnosing this life- threatening disease. This topic focuses on the application and evaluation of various machine learning algorithms for the purpose of lung cancer detection. The performance and effectiveness of algorithms, including support vector machines (SVM), random forests, deep learning models, and ensemble methods, are explored to achieve accurate classification of lung cancer cases. Support vector machines (SVM) have been widely employed in lung cancer detection due to their ability to handle high-dimensional data and effectively separate different classes. SVM algorithms leverage a hyperplane to maximize the margin between positive and negative instances, resulting in robust classification. The performance of SVM models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) is evaluated to assess their suitability for lung cancer detection. Random forests, another popular machine learning algorithm, utilize an ensemble of decision trees to classify lung cancer cases. By aggregating the predictions of multiple decision trees, random forests can reduce overfitting and improve generalization capabilities. The performance metrics of random forest models, including accuracy, precision, recall, and F1 score, are examined to gauge their effectiveness in accurately classifying lung cancer cases Keyphrases: Lung Cancer, Machine learning algorithm for lung cancer, cancer treatment
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