The second pillar of our healthcare analytics triumvirate is mathematics. We are not trying to scare you off with this list; a detailed knowledge of all of the following areas is not a prerequisite for doing effective healthcare analytics. A basic knowledge of high school math, however, may be essential. The other areas are most helpful while understanding the machine learning models that allow us to predict diseases. That being said, here are some of the significant mathematical domains that comprise healthcare analytics:

• High school mathematics: Subjects such as algebra, linear equations, and precalculus are essential foundations for the more advanced math topics seen in healthcare analytics.
• Probability and statistics: Believe it or not, every medical student takes a class in biostatistics during their training. Yes, effective medical diagnosis and treatment rely heavily on probability and statistics, including concepts such as sensitivity, specificity, and likelihood ratios.
• Linear algebra: Commonly, the operations done on healthcare data while making machine learning models are vector and matrix operations. You'll effectively perform plenty of these operations as you work with NumPy and scikit-learn to make machine learning models in Python.
• Calculus and optimization: These last two topics particularly apply to neural networks and deep learning, a specific type of machine learning that consists of layers of both linear and nonlinear transformations of data. Calculus and optimization are important for understanding for how these models are trained.

An introduction to mathematics and machine learning for healthcare analytics will be provided in Chapter 3, Machine Learning Foundations.