Enhancing Precision in Lung Cancer Diagnosis Through Machine Learning Algorithms

Abstract

Lung cancer continues to pose a significant threat worldwide, leading to high cancer-related mortality rates and underscoring the urgent need for improved early diagnosis approaches. Despite the valuable technology currently employed for lung cancer diagnosis, some limitations hinder timely and accurate diagnoses, resulting in delayed treatment and unfavorable outcomes. In this research, we propose a comprehensive methodology that harnesses the power of various machine learning algorithms, including Logistic Regression, Gradient Boost, LGBM, and Support Vector Machine, to address these challenges and improve patient care. These algorithms have been thoughtfully chosen for their ability to effectively handle the complexity of lung cancer data and enable accurate classification and prediction of cases. By leveraging these advanced techniques, our methodology aims to enhance the efficiency and accuracy of lung cancer diagnosis, enabling earlier interventions and tailored treatment plans that can significantly impact patient outcomes and quality of life. Through rigorous assessments conducted on benchmark datasets and real-world cases, our study has yielded promising results. Random Forest achieved an impressive accuracy of 97%, showcasing its ability to effectively capture complex patterns and features within the lung cancer dataset. By pushing the boundaries of medical innovation and precision medicine, we envision a future where machine learning algorithms seamlessly integrate into healthcare systems, leading to personalized and efficient care for lung cancer patients.