DEVELOPMENT OF LUNG CANCER PREDICTION MODEL USING MACHINE LEARNING TECHNIQUES

Lung cancer remains one of the most prevalent and deadly forms of cancer worldwide according to World Health Organization (WHO, 2022). Primarily due to late diagnosis and the absence of early symptoms. Early detection plays a crucial role in improving survival rates and enabling effective treatment strategies. This project focuses on the design and implementation of a Lung Cancer Prediction System that leverages machine learning algorithms to analyze patient health data and predict the likelihood of lung cancer occurrence. The system was developed to assist healthcare professionals and individuals in assessing risk levels based on both clinical and lifestyle-related factors. The research process involved several stages, including data collection, preprocessing, feature selection, model training, and evaluation. The dataset utilized contained key attributes such as age, gender, smoking habits, air pollution exposure, genetic risk, occupational hazards, and respiratory symptoms like coughing, wheezing, and shortness of breath. Various machine learning algorithms such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) were applied and compared to identify the most efficient predictive model. The Random Forest algorithm exhibited superior performance in terms of accuracy, precision, and recall, and was therefore selected for system deployment. A user-friendly web application was developed to integrate the trained model, allowing users to input relevant data and obtain instant predictive results. The system provides a non-invasive, data-driven method of identifying high-risk individuals, thereby supporting early medical intervention and preventive healthcare. Overall, the Lung Cancer Prediction System demonstrates the significant potential of artificial intelligence in enhancing clinical decision-making, reducing diagnostic delays and promoting proactive health management through early detection and risk assessment. Keywords: Lung Cancer, Machine Learning, Artificial Intelligence, Predictive Modeling, Early Detection, Random Forest, Healthcare Technology, Risk Assessment, Data Mining, Preventive Medicine