What are the Common Machine Learning Algorithms Used in Epidemiology?
Several ML algorithms are commonly used in epidemiology:
1. Linear Regression and Logistic Regression: These are used for predicting outcomes and determining the relationship between variables. 2. Decision Trees and Random Forests: These algorithms are useful for classification and regression tasks, helping to identify complex interactions between risk factors. 3. Support Vector Machines (SVM): SVM is used for classification and regression analysis, particularly in high-dimensional spaces. 4. Neural Networks and Deep Learning: These algorithms can model complex, non-linear relationships in large datasets, making them suitable for image recognition and natural language processing tasks in epidemiology. 5. K-Means Clustering: This unsupervised learning algorithm is used to identify clusters of similar cases, which can be useful for identifying outbreak hotspots.
