Explore survival analysis prediction for cancer patients in this comprehensive lecture from the "Machine Learning for Health and Disease" program. Delve into advanced techniques for leveraging clinical and lifestyle parameters to forecast patient outcomes. Learn how to apply various machine learning methods, including logistic regression, tree-based algorithms, support vector machines, Bayesian approaches, and deep networks, to biomedical and health-related data. Gain insights into analyzing diverse patient data such as X-rays, ultrasound images, and ECG measurements, as well as genomic variant analysis and pattern inference in large-scale heterogeneous datasets. Bridge the gap between mathematical modeling and clinical problems while discovering tools that can be easily adapted to analyze healthcare data.