Which Machine Learning Algorithms are Commonly Used?
Several ML algorithms are commonly applied in epidemiological studies, each with its strengths and limitations:
1. Supervised Learning Algorithms: These include regression models (e.g., linear regression, logistic regression) and classification algorithms (e.g., decision trees, random forests, support vector machines). They are used when labeled data is available, enabling the prediction of specific outcomes based on input features. 2. Unsupervised Learning Algorithms: Methods like clustering (e.g., k-means, hierarchical clustering) and dimensionality reduction (e.g., PCA, t-SNE) are used to identify patterns or group similar data points without predefined labels. 3. Ensemble Methods: Techniques like bagging and boosting combine multiple models to improve predictive performance and robustness. 4. Neural Networks and Deep Learning: These advanced algorithms are particularly effective for complex tasks such as image recognition and natural language processing, making them useful in analyzing medical imaging and unstructured health data.
