Lung Cancer Prediction using Machine Learning Methods

Abstract

Lung cancer, being a major global health issue, requires not only effective treatments but also robust predictive methods to enable early diagnosis and preventive measures. Detecting the disease at an early stage increases survival chances and reduces the necessity for aggressive treatments with harsh side effects. This paper seeks to contribute to the ongoing research by utilizing machine learning techniques to analyze a variety of factors that influence the severity of lung cancer, such as smoking habits, air pollution exposure, genetic predispositions, and lifestyle choices. By leveraging the lung cancer dataset sourced from Kaggle, this study aims to develop predictive models that can help identify individuals at high risk for lung cancer. These models could be utilized by healthcare professionals to provide early interventions, ultimately improving patient outcomes and reducing the burden of late-stage diagnoses. Through the application of data-driven insights, this research hopes to highlight key risk factors and demonstrate the value of integrating machine learning into the early detection and prevention of lung cancer.