AI-Driven Insights: A Survey on Innovative Approach for Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods : Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods

The lungs are vital for exchanging oxygen and carbon dioxide, providing oxygen to cells for energy production, and removing waste carbon dioxide from the body.They also help filter and protect the body from harmful particles and pathogens in the air.Proper lung function is essential for overall health and well-being.Lung cancer ranks as the second most common cause of cancer-related fatalities globally.It emphasizes the challenge of early detection due to the lack of symptoms in the initial stages.Which contributes to a high mortality rate compared to other lung diseases.Early identification and precise prediction of lung cancer serves as vital for improving patient outcomes. Machine learning (ML) techniques have shown great promise in revolutionizing lung cancer prediction by leveraging various data sources such as medical images, clinical data, and genomic information. This paper presents a review of lung cancer prediction using ML, DL, and AI methods.Accurate classification of lung cancer through medical imaging is essential for physicians to determine appropriate treatment options, ultimately aiming to reduce cancer mortality.The goal is to compare the performance and accuracy of convolutional neural networks (CNNs), Random forest algorithm, ensemble extreme boosting (XGBoost) algorithm,Support Vector Machine (SVM), AdaBoost classification model (ADB-C), Long Short-Term Memory Networks (LSTM), incremental multiple resolution residual network (iMRRN) models while also providing explanations for their predictions.