A multimodal prediction model for suicidal attempter in major depressive disorder.

ABSTRACT:

Background

Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts.

Methods

Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not.

Result

SUBMITTER: Li Q 

PROVIDER: S-EPMC10638918 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature