Analyzing Cancer Prognosis with Advanced Machine Learning Models

This study investigates the use of Support Vector Machines (SVM) and other machine learning algorithms for predicting the prognosis of lung, breast, and cervical cancer patients. The research evaluates the predictive accuracy, influential features, algorithm performance, and model generalization across different cancer types. Using publicly available datasets, the study highlights SVM's exceptional accuracy in prognosis prediction, with findings indicating its superiority over alternative algorithms. Notably, it identifies the key clinical, molecular, and pathological features that significantly impact predictive accuracy. The study also discusses the clinical applicability of these models, emphasizing their potential to aid healthcare professionals in making more informed treatment decisions. Acknowledging limitations, including data availability and computational resources, the study suggests future directions, encouraging the exploration of additional techniques, diverse datasets, and real-world clinical trials to validate the model’s effectiveness. Keywords Cancer, Medical Diagnosis, Markers, Learning, Patients, Machine Learning, Support Vector Machine