Diagnosis and Prognosis of Lung Cancer & Lung Nodule using Machine Learning Techniques

Abstract

Lung cancer is a serious and challenging cancer to diagnose. It frequently results in death in both men and women; thus, prompt, precise nodule analysis is crucial to the course of treatment. Early cancer detection has been accomplished through a variety of techniques. This research compares machine learning techniques for lung cancer nodule detection. To find anomalies, we used machine learning techniques such as principal component analysis, K-nearest neighbors, support vector machines, Naïve Bayes, decision trees, and artificial neural networks. We examined every technique with and without preprocessing. According to the experimental results, decision trees produce the most accurate results with 93,24% effectiveness without image processing while artificial neural networks produce the finest results with 82,43% effectiveness after image processing.