An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer

TL;DR

This paper reviews how machine learning methods, combined with feature selection techniques like Chi-squared test, can improve the accuracy and efficiency of early lung cancer detection.

Contribution

It analyzes the integration of feature selection and machine learning models such as RF and SVM for lung cancer diagnosis, providing insights for future improvements.

Findings

Enhanced detection accuracy using RF and SVM

Feature selection improves model performance and runtime

Recommendations for future research in automated diagnosis

Abstract

One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer using sophisticated machine learning techniques like Random Forest (RF) and Support Vector Machine (SVM). The Chi-squared test is one feature selection strategy that has been successfully applied to find related features and enhance model performance. The findings demonstrate that these techniques can improve detection efficiency and accuracy while also assisting in runtime reduction. This study produces recommendations for further research as well as ideas to enhance diagnostic techniques. In order to improve healthcare and create automated methods for detecting lung cancer, this research is a critical first step.