Machine learning algorithms and techniques for medical applications. Comprehensive guides on regression, classification, ensemble models, decision trees, and predictive modeling in healthcare.
Introduction The softmax function is essential in mathematics and machine learning. It transforms a vector of real numbers into a probability distribution. Put simply, it converts a set of numbers into probabilities. To understand how it works, let’s look at a list of risk values for some patients: Patients Surgical Risk Patient A 2 Patient … Read more
Introduction UNet is a convolutional neural network (CNN) introduced by Olaf Ronneberger in 2015. These specialized neural networks learn to recognize objects in images. When properly trained, they can analyze medical images, detect specific features (such as neoplasms in CT scans), and classify different types of images (such as distinguishing between pneumonia and neoplasms). In … Read more
Definition Sensitivity analysis is a collection of techniques that determine how input parameters affect model results. Specifically, it measures how much variation in the results stems from different types of uncertainty. For a model: $Y=f(X_1,X_2,X_3…..X_n)$ examines how Y changes when each X is modified. Sensitivity analysis can be applied across several key areas: predictive models, … Read more