Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting

Abstract

Rationale: Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior.Objectives: The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults.Methods: The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability.Results: From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R2.Conclusions: Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy

“…Of the studies included in the systematic review, six assessed predictors of criminal recidivism [20][21][22][23][24][25], two assessed predictors of the type of criminal offence [26,27], three assessed predictors of physical violence during inpatient stay [28][29][30], and six assessed predictors of violent offending and aggression following discharge [24,[31][32][33][34][35][36][37][38]. All studies, apart from two [21,30], used clinical input features, including socio-demographic information, questionnaires, and psychometric measures to derive predictions.…”

Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness

“…The research team used a two-stage applied machine learning approach for variable selection and model building to predict first drink of the day. This approach has demonstrated effective performance in prior research ( Bauer et al, 2019 ; R. Suchting, Gowin, Green, Walss-Bass, & Lane, 2018 ; Suchting, Hébert, Ma, Kendzor, & Businelle, 2019 ; Walss-Bass, Suchting, Olvera, & Williamson, 2018 ). We used successive passes through two algorithms (component-wise gradient boosting and backward elimination) to reduce a set of random and daily EMA predictors of imminent drinking.…”

Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment

“…The number of algorithm iterations is determined using 10-fold cross-validation, with two consequences: (1) optimized predictive performance via limited overfitting and (2), an inherent variable selection capacity, as only so many predictors may be chosen in the finite set of iterations before the algorithm terminates. Recent research has demonstrated the utility of the CGB algorithm for deriving optimized, parsimonious models of outcomes in health and behavioral sciences; examples include determinations of the best (a) inflammatory predictors of adolescent depression and anxiety [ 54 ], (b) psychosocial and genetic predictors of aggression [ 55 ] and (c) cognitive test predictors of pediatric bipolar disorder [ 56 ].…”