An Extensive Analysis of Machine Learning Techniques for Predicting the Onset of Lung Cancer

Abstract

This research paper conducts an extensive analysis of various machine-learning techniques employed in predicting the onset of lung cancer. Lung cancer is a leading cause of mortality worldwide, emphasizing the critical need for accurate and timely prediction methods. Several machine-learning algorithms, such as SVM, RF, neural networks, and ensemble techniques, have been explored in this research. Through a comprehensive review and comparative study, this paper evaluates the effectiveness of different machine learning models in predicting lung cancer onset based on various input features, such as demographic data and medical history. The effectiveness of these methods is assessed using important metrics like accuracy, sensitivity, F1-score, computational efficiency, and the area under the receiver operating characteristic curve (AUC-ROC).Continuing technological progress is changing how healthcare operates, with the incorporation of machine learning techniques showing great promise in detecting issues early and predicting outcomes.

Furthermore, feature selection methods and data pre-processing techniques are explored to enhance prediction accuracy and reduce computational complexity. The results of this study offer important insights into the efficiency of machine learning for lung cancer prediction and suggest recommendations for future research.