An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer
This addresses early diagnosis of lung cancer for improved patient outcomes, but is incremental as it reviews existing methods.
This review examined how combining feature selection methods like Chi-squared tests with machine learning algorithms (Random Forest and SVM) can improve lung cancer detection, reporting enhancements in accuracy, efficiency, and runtime reduction.
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.