Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study

Objectives Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the development of hypertension.Methods A total of 15 965 Ja...

Full description

Saved in:
Bibliographic Details
Main Authors: Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Clinical and Experimental Hypertension
Subjects:
Artificial intelligence
machine learning
hypertension
fatty liver index
Online Access:https://www.tandfonline.com/doi/10.1080/10641963.2025.2449613
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.tandfonline.com/doi/10.1080/10641963.2025.2449613

Similar Items