Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

ABSTRACT: In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis know

SUBMITTER: Jalali-Najafabadi F 

PROVIDER: S-EPMC8640070 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature