COMPARATIVE ANALYSIS TOWARDS PREDICTION OF LUNG CANCER USING MACHINE LEARNING MODELS

Abstract

One of the most prevalent cancers that negatively affects a person's health is lung cancer. It is a condition in which certain cells in the lungs develop abnormally and multiply rapidly, resulting in the formation of a tumor. If lung cancer is discovered in its initial stages, several lives can be saved. The number of human deaths will keep increasing if this disease is not detected and treated before the second stage. This research paper is mainly intended to do a comparative study and detect lung cancer at earlier stages through various data pre-processing techniques and machine learning algorithms like Logistic Regression, K-Nearest Neighbour(KNN), Support Vector Machine(SVM), Decision Tree, and Random Forest(RF). It was observed that Logistic Regression is the most accurate mode with accuracy 96%l. The best model was deployed for predicting future trends in real-time providing a user interface using Flask, a Python web framework, which accepts patient information as input and predicts whether or not a person has lung cancer.