Scrutinizing Machine Learning Models For Cancer Prediction

Abstract

Due to some abnormal changes in genes of cells, enforces cells to divide uncontrollably, due to which tumors are formed, which infiltrates and damages the normal body tissues, and this condition is called "Cancer". Lung cancer is a type of cancer where the infected cells in the lungs multiply rapidly at a high rate. This abnormal growth of cells, which eventually leads to cancer can be identified using modern data analysis. Detecting cancer symptoms at an early stage plays a crucial role for the patients who may suffer later, if not detected. One of the major problems is the increasing fad of smoking tobacco in youngsters. Air pollutants from industries which get inhaled by people are some of the main causes of increasing lung cancer in India. The main focus of this study is to predict lung cancer in different patients using Machine Learning (ML) algorithms such as a random forest classifier(RFC), k-nearest neighbour(KNN), K-means, Support vector machine(SVM), and decision tree classifier(DTC). The key objective of this research is the analysis of different machine learning algorithms based on their performance metrics.