Improving the assessment of older adults using feature selection and machine learning models

Abstract

Purpose The growing capacity of healthcare systems to digitize patient information is enabling the creation of large repositories of patient health data, facilitating the use of Artificial Intelligence techniques, especifically Machine Learning, to analyze this data for insights and discovery. Thanks to this, unprecedented predictions and accurate diagnosis of certain diseases are possible to achieve today. However, this increasing morass of information is a double-edged sword as it makes it difficult for health professionals to navigate and determine which information is most crucial to examine for a given pathology or health condition. Feature Selection techniques have been applied for years to help Machine Learning prediction models to determine which information is most relevant to diagnoses, as demonstrated in Remeseiro et al. (2019). Consequently, these techniques help reduce the amount of information that health professionals need to collect, reducing laborious work while making them aware of which factors are more important for the assessment in contrast with what they initially considered to be important or relevant