Establishing a machine learning dementia progression prediction model with multiple integrated data.

ABSTRACT:

Objective

Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerative dementia are highly valuable. Machine learning (ML) methods facilitate the construction of effective models using real-world data, which may include missing values and various integrated datasets.

Method

This retrospective study analyzed data from 679 patients diagnosed with degenerative dementia at Fu Jen Catholic University Hospital, who were evaluated by neurologists, psychologists and followed for over two years. Predictive variables were categorized into demographic (D), clinical dementia rating (CDR), mini-mental state examination (MMSE), and laboratory data value (LV) groups. These categories were further integrated i

SUBMITTER: Huang YC 

PROVIDER: S-EPMC11583646 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature