Stroke is one of the most expensive and dangerous conditions today. See how machine learning and analytics can contribute to stroke care. Along the way, you’ll learn more about random forests, a machine learning classifier, and cost-sensitive learning, a technique for tuning machine learning models.
Learn how to identify high-risk asthmatic patients utilizing both predictive analytics and clinical knowledge. Along the way, you’ll learn more about logistic regression, which is a technique widely utilized in both biostatistics and machine learning.
Diabetes is a huge burden among vulnerable populations. Leverage the latest analytic tools and techniques to predict diabetes in the Pima Indian population. Along the way, you’ll learn more about gradient boosting machines, a powerful algorithm that has enjoyed success in numerous domains.
Use predictive analytics and machine learning to both predict no-show appointments and understand the factors behind medical no-show appointments. Along the way, you’ll learn more about decision trees, an intuitive yet effective predictive algorithm.
Heart disease is one of the most common conditions in the world. In this case, you’ll utilize analytics to evaluate and predict heart disease in a population from three different countries. Along the way, you’ll learn more about naive Bayes algorithms, a simple yet powerful predictive model.