What are the Key Machine Learning Techniques Used?
Supervised Learning Supervised learning involves training models on labeled data. In epidemiology, this might include datasets where outcomes (such as disease presence) are already known. Common techniques include linear regression, logistic regression, and random forests. These models are used for tasks like predicting disease outbreaks or identifying risk factors associated with certain health outcomes.
Unsupervised Learning Unsupervised learning does not rely on labeled data. Instead, it seeks to identify underlying patterns or groupings within the data. Techniques such as clustering (e.g., k-means clustering) and principal component analysis (PCA) are used to segment populations or reduce the dimensionality of large datasets, respectively.
Deep Learning Deep learning, a subset of ML, uses neural networks with multiple layers to process data. Techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly useful in handling complex, high-dimensional datasets, such as medical imaging and time-series data. These models have shown promise in tasks ranging from disease detection to predicting the spread of infectious diseases over time.
