An Intelligent System for Prediction of Lung Cancer Under Machine Learning Framework

In recent years, there has been a significant increase in the prevalence and severity of lung cancer, rendering it a grave health concern. Lung cancer stands as the primary cause of mortality associated with cancer on a global scale. Early detection is of utmost importance as the failure to do so may lead to fatality. Lung cancer metastasis was observed in both male and female individuals. This paper presents a machine learning-based model developed for the purpose of predicting lung cancer. The primary aim of this study is to construct a predictive model that can effectively and promptly forecast the occurrence of lung cancer. The suggested endeavour has employed many machine learning algorithms, including support vector machine (SVM), logistic regression, decision tree classification, K-nearest neighbours (KNN), Gaussian NB, and artificial neural network. The highest level of accuracy, reaching 99%, has been attained using decision tree and artificial neural network methodologies. The originality of this study is in the application of various machine learning approaches and their comprehensive comparative analysis in the prediction of lung cancer. This research endeavour aims to enhance the efficiency of the treatment process by facilitating early and precise disease prediction.