Prediction of transition from mild cognitive impairment to Alzheimer's disease based on a logistic regression-artificial neural network-decision tree model.

Aim: To develop a logistic regression model, artificial neural network (ANN) model and decision tree (DT) model for the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) to compare the performance of the three models.

Methods: A total of 425 patients with MCI were screened from the original cohort. The actual follow up included 361 patients, with AD as the outcome variable.

Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests.

Aim: The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests.

Methods: A total of 3657 community-dwelling adults aged ≥60 years from Nanchang city were recruited in this study. A 3-year follow-up test was carried out for all the participants to determine whether they turn to non-SA.

Reference value for the TUGT in healthy older people: A systematic review and meta-analysis.

The timed up and go test (TUGT) was recently proposed as a strong predictor of adverse outcomes. Few reviews have been conducted to identify a standard for the TUGT in healthy older people, and the aims of this study were to explore the source of heterogeneity and evaluate the range of reference values for the TUGT in healthy people over 60 years old stratified by age and sex. The VIP, EMBASE, Web of Science and PubMed databases were searched from January 1, 2000, to December 31, 2018.