A Survey on Lung Cancer Prediction

Abstract

Cancerous tissue that develops in lung is called a lung cancer. It's the primary reason people die from cancer, everywhere, with smoking being the primary cause of the disease. Lung cancer prediction involves using various methods and technologies is to identify the individuals who are at higher risk of developing the disease. The goal of lung cancer prediction is to detect the disease early, when it is most treatable, or to prevent it from developing altogether. Machine learning may be used to forecast the risk that someone will acquire lung cancer by analysing vast volumes of data to find patterns. In this study, a comparison of various ML-based algorithms for detecting lung cancer has been published. The techniques were used to look for cancer. There has been an explosion in the number of techniques available in recent years for diagnosing lung cancer. The majority of these techniques make use of CT scan pictures, while some make use of x-ray images. In addition, many different classifier strategies are combined with a wide variety of segmentation techniques in order to employ image recognition for the purpose of locating lung cancer nodules. According to the findings of this research, multi-gene genetic programming is superior to other approaches in terms of producing reliable outcomes.