What is a Longitudinal Study?
How does a Longitudinal Study work?
Imagine wanting to observe changes in optimism and attitude over time. One could use sets of individuals from various age ranges (20s, 30s, 40s) and compare their attitudes, however there may be confounding variables. For instance, the 30-year olds may be an unusually cynical group of individuals, skewing the data. In order to minimize these confounding factors, one could turn to a longitudinal study. In that case, the experimenter would observe a single group of individuals throughout their 20s, 30s, and 40s. This way, the same individuals are being measured over time, which improves accuracy of measurement and avoids confounding variables.
Longitudinal Studies and Machine Learning
While longitudinal studies themselves don't have a direct correlation to machine learning, their data does. Machine learning algorithms can use longitudinal data to understand and infer trends, changes over time, and possibilities of specific occurrences. For example, machine learning has been implemented inprediction of cardiovascular disease
using longitudinal genetic data. The incorporation of deep learning technology has led to improved predictions of cardiovascular disease and an enriched understanding of the importance of genetic markers in understanding health risks.