PREDICTION LUNG CANCER BASED CRITICAL FACTORS USING MACHINE LEARNING

Abstract

Many people around the world have lung cancer. Lung cancer has a poor prognosis and a high mortality rate. Through image recognition and data analytics, computers can play a significant role in detecting various types of cancer disease. This paper provides an effective method to predict lung cancer in an early stage with high accuracy ratio. This research proposed data analytics to determine the accuracy ratio of lung cancer patients using supervised machine learning algorithms (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The dataset for this study was obtained from "Data World," which contains 1,000 diseases. Machine learning algorithms enable us to identify lung cancer risk factors, which aid in diagnosing lung cancer. This study shows that those algorithms can classify lung cancer patients, with Random Forest having the highest accuracy of 98.507%.