Publisher's Synopsis
Lung cancer is a major cause of cancer-related deaths worldwide. Machine learning techniques have shown promising results in the early detection and prediction of lung cancer. However, high-dimensional data, such as gene expression profiles, can introduce noise and decrease the classification accuracy of machine learning models. Feature selection techniques can alleviate this issue by identifying the most relevant and informative features, leading to better model performance.
Optimized feature selection techniques can enhance the prediction accuracy of lung cancer using machine learning algorithms. Support vector machines, random forest, and artificial neural networks are commonly used algorithms for lung cancer prediction. By optimizing feature selection, these models can be trained with the most informative features, reducing overfitting and improving classification accuracy.
Cross-validation techniques can also be used to evaluate the performance of feature selection and machine learning algorithms. The integration of optimized feature selection with machine learning techniques can provide an accurate and reliable lung cancer prediction model, which has the potential to improve early detection and precision medicine for lung cancer patients.
Overall, optimized feature selection for enhancing lung cancer prediction using machine learning techniques is a promising approach to improving patient outcomes and reducing the global burden of lung cancer.
