Comprehensive survey on Exploratory Data Analysis and Machine Learning Approaches for Lung Cancer Detection

Abstract

Lung cancer continues to be a leading cause of death, and hence there is a significant need for early detection to improve survival rates. This current research addresses some loopholes existing in the current diagnostic methods, namely overfitting and poor generalization capabilities, by integrating the techniques of exploratory data analysis and machine learning. This research employs regression algorithms and K-Nearest Neighbors (KNN) enhanced with Principal Component Analysis (PCA) to attain a classification accuracy of 95%. The proposed framework offers a scalable solution for the precise detection of lung cancer and addresses challenges in clinical diagnostics.