Innovative Machine Learning Algorithms for AI-Driven Lung Cancer Diagnostics and Prognostics

Lung cancer is the most dangerous cancer in the world, and therefore early detection and a correct prognosis are necessary to ensure that patient outcomes are improved. The development of technology in structured medical information, such as patient lifestyle data and symptoms, holds promise for artificial intelligence (AI) to transform diagnostic accuracy. Machine learning algorithms (ML) can learn to recognize patterns to facilitate timely and evidence-based clinical decisions. Traditional diagnostic techniques always result in delayed detection, high false positive rates, and inconsistent evaluations. These are a result of the failure to compare multiple risk indicators effectively. There is a strong need for computer systems that integrate multiple patient information in a bid to facilitate reliable early diagnosis. In this paper, several ML algorithms are evaluated, including K-Nearest Neighbors (KNN), Polynomial Logistic (PL), Logistic Regression (LR), Decision Tree, Random Forest (RF) and Support Vector Machines (SVM) using two structured lung cancer datasets. These data sets include variables such as age, smoking, exposure to pollution, and symptoms. The aim is to identify models that provide high accuracy in lung cancer prediction. The results show that Random Forest and SVM achieved the best accuracy, sensitivity, and specificity. Key predictive features include smoking, long-term illness, and exposure to pollution. Proper pre-processing and feature selection improved performance across all models. This study demonstrates the ability of machine learning, such as Random Forest greatly enhances lung cancer prediction with the aid of structured lifestyle and clinical information. Continued development and clinical collaboration could make these models valuable tools for early diagnosis and patient prognosis.