Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.

ABSTRACT:

Background

Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypothesized that ML identifies the health, psychological, psychiatric, and contextual features to predict SUD, and the identified features predict high-risk individuals to develop SUD.

Method

Male (N = 494) and female (N = 206) participants and their informant parents were administered a battery of questionnaires across five waves of assessment conducted at 10-12, 12-14, 16, 19, and 22 years of age. Characteristics most strongly associated with SUD were identified using the random forest (RF)algorithm from approximately 1000 variables

SUBMITTER: Jing Y 

PROVIDER: S-EPMC6980708 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature