Data-Driven Lung Cancer Staging: Integrating Clinical Parameters and Molecular Insights for Accurate Detection

Lung Cancer diagnosis and treatment have made significant advancements in recent years, with the introduction of targeted chemotherapy and precise radiotherapy techniques. Nevertheless, improving the cancer patient’s survival rate is still a big challenge. To meet the required challenges of improving the survival rate of lung cancer patients, we developed machine learning models to predict the level of lung cancer based on various risk factors and biomarkers. This model utilizes a combination of supervised learning algorithms to examine patient records and identify patterns that may indicate the presence of the level of lung cancer. By training the model on a diverse dataset of patient records and genetic information, we aim to improve the accuracy of early detection and provide personalized treatment recommendations. Our approach integrates features such as location, histology, patient demographics, and lifestyle factors to create a comprehensive and robust predictive tool. The machine learning model holds the potential to identify the way lung cancer is diagnosed and managed, ultimately contributing to better patient outcomes and enhanced overall survival rates.