Enhanced Lung Cancer Detection using Support Vector Machine Algorithms

Still one of the biggest causes of mortality linked to cancer worldwide is lung cancer. Improving treatment efficacy and survival rates requires early identification. Recent advances in ML and AI provide considerable potential to improve lung cancer diagnosis. The aim of this work is for improved lung cancer detection accuracy or efficiency by use of Support Vector Machine (SVM) methods. Effective but expensive and prone to mistakes are conventional methods for diagnosis include biopsy specimen's chest X-rays, and CT scans. Usually requiring much manual learning, these methods increase human error. Applying ML techniques, especially SVMs allows a streamlined and enhanced diagnostic process, therefore maybe generating more accurate and quick diagnosis. This work summarizes results of several ML and artificial intelligence approaches applied for lung cancer diagnosis, stressing the advances attained by means of reconstructed independent component analysis, sparse filter options, pretreatment and extraction of characteristics, transfer learning, and optimization techniques including Particle Swarm Optimization (PSO). Deep learning, hybrid models, and computational image processing approaches taken together have greatly boosted the subject by proving superior detection accuracy and resilience. This work aims to contribute to early identification and treatment by means of a precise and efficient SVM-based lung cancer prediction system, thereby improving patient outcomes. Future studies should keep investigating and enhancing these methods to raise their flexibility, dependability, and use in clinical settings.