Support Vector Machines are machine learning algorithms commonly used in healthcare for classification and regression tasks. In healthcare, support vector machines excel in pattern recognition and classification problems, such as diagnosing diseases, analyzing medical images, and identifying genetic markers for specific conditions. The key advantage of support vector machines is their ability to find the optimal boundary, or hyperplane, separating different data points in high-dimensional space. This is particularly useful in healthcare, where datasets often contain many features, such as patient demographics, clinical symptoms, and laboratory results. Support vector machines also effectively handle non-linear relationships through kernel functions, allowing them to adapt to medical data’s complex and varied nature. One of the critical advantages of support vector machines is their robustness against overfitting, especially in cases where the number of features exceeds the number of samples. However, choosing the kernel function and tuning parameters requires careful consideration to ensure the model’s effectiveness. Despite these challenges, support vector machines are highly regarded for their accuracy and efficiency in various medical applications, from disease detection to outcome prediction. They play a significant role in advancing predictive analytics in healthcare.