Predicting a few or many friends in schoolchildren
A machine learning approach
Bibliographic Data
| ID | 22411131 |
|---|---|
| Authors | José Ventura-León (0000-0003-2996-4244, Ciencias de la Salud, Universidad Privada del Norte, corresponding author), Goldie Gamboa‐Melgar (0000-0003-4972-5777, Psicología, Universidad Peruana de Ciencias Aplicadas), Jonathan Ruiz-Castro (0000-0003-1800-2533, Escuela de Psicología, Universidad Continental), Cristopher Lino‐cruz (0000-0001-7873-2402, Psicología, Universidad Peruana de Ciencias Aplicadas), Shirley Tocto‐muñoz (0000-0002-4485-5639, Ciencias de la Salud, Universidad Privada del Norte) |
| Year | 2026 |
| Pages | 1-16 |
| Publication date | 2026-06-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of General Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0022-1309 • E-ISSN: 1940-0888 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00221309.2026.2687378 |
| PMID | 42313573 |
| OpenAlex | W7165147553 |
| Language | EN |
| References cited | 60 |
Machine learning algorithms were used to determine whether Peruvian 8–12-year-olds (N = 730) believe they have many or few friends based on sociodemographic and psychological variables. Theoretical variables were depression (PHQ-8), subjective well-being (SWB-3), sorrow, bullying, and birthplace. With stratified 10-fold cross-validation, the easyML R package examined six algorithms: Random Forest, XGBoost, Support Vector Machine, Neural Network, Logistic Regression, and Decision Tree. The cross-validation pipeline used SMOTE to correct class imbalance (25.6% few vs. 74.4% many friends). Logistic Regression had the greatest ROC-AUC (0.663 in cross-validation, 0.673 on the test set) and no overfitting (CV-test gap = −0.010). The greatest SHAP predictor was subjective well-being, followed by depression and birthplace. Youden’s J index threshold optimization (optimal = 0.44) increased classification accuracy from 64.6% to 72.8%. Psychology predicts friendship perception in youngsters, with well-being outweighing clinical symptomatology.
Artificial neural network · Class (philosophy) · Logistic regression · Overfitting · Perception · Pipeline (software) · Random forest · Support vector machine · Attachment and Relationship Dynamics · Bullying, Victimization, and Aggression · Child and Adolescent Psychosocial and Emotional Development
Childhood peer relationships
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Parental Attachment and Peer Relationships in Adolescence
Choosing Prediction Over Explanation in Psychology
Machine Learning Approaches for Clinical Psychology and Psychiatry
The Contributing Role of Family, School, and Peer Supportive Relationships in Protecting the Mental Wellbeing of Children and Adolescents
Peer Victimization, Poor Academic Achievement, and the Link Between Childhood Externalizing and Internalizing Problems
Social Withdrawal in Childhood
Peer Relationships in Adolescence
Feature Selection with the Boruta Package
Smote
Recommendations for the extraction, analysis, and presentation of results in scoping reviews
The global landscape of AI ethics guidelines
A Brief Measure for Assessing Generalized Anxiety Disorder
Positive psychology
The PHQ-8 as a measure of current depression in the general population
Machine Learning and Psychological Research
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Relationship between Socio-Emotional Competencies and the Overlap of Bullying and Cyberbullying Behaviors in Primary School Students
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The Predictive Role of Preschool Children’s Attachment on Social Competence, Anxiety, Aggression and Self-Control
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Associations between Adolescents’ Interpersonal Relationships, School Well-being, and Academic Achievement during Educational Transitions
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The ethics of algorithms
| Citation velocity | historical |
|---|---|
| Highly cited | No |