Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1.

ABSTRACT:

Objective

Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).

Materials and methods

EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode

SUBMITTER: Gupta A 

PROVIDER: S-EPMC11752863 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature