Comparison of Feature Extraction, Data Preprocessing, and Predictive Modeling for Enhancement of Clinical Lung Cancer Detection. Lung cancer remains a significant global health burden, requiring rapid and accurate diagnosis. In this comparative study, the human-centered focus of hospital implementation of these algorithms is highlighted. The first step of the investigation is feature extraction. We investigate edge detection, texture classification, and deep-learning-based convolutional neural networks. We compare the recognition accuracy of their lung imaging data, an important feature for the early detection of cancer. Then the research examines data preparation techniques. Good data is, therefore, paramount for diagnostic accuracy. We normalize, enhance, and reduce noise. We will investigate the impact of various preprocessing approaches on the improved detection of lung cancer, particularly when clinical data is not available or contains noise. The study ends with predictive algorithms. Compare logistic regression, SVM, and RNNs. This comparison is based on prediction accuracy, computational efficiency, and clinical workflow integration. In fact, this human-centered study demonstrates the applicability of these algorithms as well as their utility in hospitals. The analysis should consider computation cost, medical expert interpretability, and integration with existing medical imaging systems. The remaining part of this study deals with algorithm comparison for each lung cancer detection phase. Evaluating the algorithms’ theoretical superiority and actual real-world impact on lung cancer detection and patient outcomes guides clinicians and researchers on how best to select the right technology.