# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 1 Introduction  
The incidence and mortality due to uncontrolled cancer are critical public health issues in India and have been increasing, meaning that a significant proportion of deaths is due to cancer. About 75,000 new cases of cancer were reported in the year 2022. This is further compounded by a high prevalence of risk factors combined with challenges in the early detection and diagnosis of the disease [1]. Tobacco use is the leading cause of lung cancer in India, and it holds about 85% of cases. According to the 2016-17 GATS report, an estimated 28.6% of adults in India are tobacco users in any form, with a higher percentage among men [2]. It includes smoking cigarettes, bidis, and smokeless tobacco products like gutka and paan. Moreover, exposure to secondhand smoke raises the risks of carcinogenic agents, which, in turn, raises the chances of lung cancer [3]. Lastly, environmental pollution is a significant cause of growing lung cases in India. The top polluted cities are Delhi, Mumbai, and Kolkata, which are among the worst pollutedcities worldwide. Emissions from automobiles and industrial activities and particulate matter emitted during construction significantly damage the lungs and are associated with an increased incidence of lung cancer. Evidence has shown that exposure to ambient PM2.5 levels can be associated with a further increased risk of lung cancer, according to a published study in The Lancet Planetary Health [4]. Household pollution is one of the significant rural concerns because more people use mainly solid fuels such as firewood, cow dung, and coal to cook and heat their homes. The WHO said that an estimated 700 million people in India will be exposed to household air pollution; this increases lung cancer risks, especially for women who spend more of their time in the kitchen [5]. Occupational hazards are one of the major causes that have led to the development of lung cancer. People who work in the construction, mining, and manufacturing industries are usually exposed to cancer-causing carcinogens, such as asbestos, radon gas, and many others. In this respect, strict safety measures within occupational places, along with annual checkups, can minimize the threat of such occupational hazards to a greater extent [6]. If lung cancer is discovered early, the survival rate can be higher. The five-year survival rate for lung cancer diagnosed at an early stage can reach between 60% and 80%, whereas the survival rate for advanced-stage lung cancer falls below 15% [7]. The National Programme for the Prevention and Control of Cancer and Diabetes is designed to enhance cancer screening and early detection initiatives throughout India. However, more than 70% of lung cancer diagnoses occur at the late stages, and therefore, more advanced screening and diagnostic techniques are fundamentally required [8]. Current developments in ML and AI open exciting possibilities for better detection and diagnosis of lung cancer. Algorithms in machine learning can identify insights from large datasets, risk factors associated with conditions, and other identifiable patterns, which allows for early detection that leads to personalized treatment planning. Recently, a study in The Lancet Oncology showed how the AI-based system reduced false positives and negatives for lung cancer screening and increased accuracy levels [9]. The present study focuses on developing a hybrid model for lung cancer prediction while also conducting a comparative analysis of various machine learning algorithms. The proposed preprocessing techniques, including ADASYN and label encoding, enhance model performance by addressing class imbalance and feature representation. This study aims to improve early detection and diagnosis by integrating advanced machine learning techniques to analyze key risk factors associated with lung cancer. By leveraging a hybrid approach that combines traditional machine learning models with deep learning, the research seeks to achieve higher predictive accuracy.