Predicting adolescent psychopathology from early life factors: A machine learning tutorial
Objective: The successful implementation and interpretation of machine learning (ML) models in epidemiological studies can be challenging without an extensive programming background. We provide a didactic example of machine learning for risk prediction in this study by determining whether early life...
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Elsevier
2024-12-01
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| Series: | Global Epidemiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590113324000270 |
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| author | Faizaan Siddique Brian K. Lee |
| author_facet | Faizaan Siddique Brian K. Lee |
| author_sort | Faizaan Siddique |
| collection | DOAJ |
| description | Objective: The successful implementation and interpretation of machine learning (ML) models in epidemiological studies can be challenging without an extensive programming background. We provide a didactic example of machine learning for risk prediction in this study by determining whether early life factors could be useful for predicting adolescent psychopathology. Methods: In total, 9643 adolescents ages 9–10 from the Adolescent Brain and Cognitive Development (ABCD) Study were included in ML analysis to predict high Child Behavior Checklist (CBCL) scores (i.e., t-scores ≥ 60). ML models were constructed using a series of predictor combinations (prenatal, family history, sociodemographic) across 5 different algorithms. We assessed ML performance through sensitivity, specificity, F1-score, and area under the curve (AUC) metrics. Results: A total of 1267 adolescents (13.1 %) were found to have high CBCL scores. The best performing algorithms were elastic net and gradient boosted trees. The best performing elastic net models included prenatal and family history factors (Sensitivity 0.654, Specificity 0.713; AUC 0.742, F1-score 0.401) and prenatal, family, history, and sociodemographic factors (Sensitivity 0.668, Specificity 0.704; AUC 0.745, F1-score 0.402). Across all 5 ML algorithms, family history factors (e.g., either parent had nervous breakdowns, trouble holding jobs/fights/police encounters, and counseling for mental issues) and sociodemographic covariates (e.g., maternal age, child's sex, caregiver income and caregiver education) tended to be better predictors of adolescent psychopathology. The most important prenatal predictors were unplanned pregnancy, birth complications, and pregnancy complications. Conclusion: Our results suggest that inclusion of prenatal, family history, and sociodemographic factors in ML models can generate moderately accurate predictions of adolescent psychopathology. Issues associated with model overfitting, hyperparameter tuning, and system seed setting should be considered throughout model training, testing, and validation. Future early risk predictions models may improve with the inclusion of additional relevant covariates. |
| format | Article |
| id | doaj-art-b5e5f3590cea4bbda9154f798e7627cf |
| institution | OA Journals |
| issn | 2590-1133 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Global Epidemiology |
| spelling | doaj-art-b5e5f3590cea4bbda9154f798e7627cf2025-08-20T02:34:20ZengElsevierGlobal Epidemiology2590-11332024-12-01810016110.1016/j.gloepi.2024.100161Predicting adolescent psychopathology from early life factors: A machine learning tutorialFaizaan Siddique0Brian K. Lee1Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, United States of America; Conestoga High School, Berwyn, PA, United States of AmericaDepartment of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, United States of America; Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden; Corresponding author at: 3215 Market St, Philadelphia, PA 19104, United States of America.Objective: The successful implementation and interpretation of machine learning (ML) models in epidemiological studies can be challenging without an extensive programming background. We provide a didactic example of machine learning for risk prediction in this study by determining whether early life factors could be useful for predicting adolescent psychopathology. Methods: In total, 9643 adolescents ages 9–10 from the Adolescent Brain and Cognitive Development (ABCD) Study were included in ML analysis to predict high Child Behavior Checklist (CBCL) scores (i.e., t-scores ≥ 60). ML models were constructed using a series of predictor combinations (prenatal, family history, sociodemographic) across 5 different algorithms. We assessed ML performance through sensitivity, specificity, F1-score, and area under the curve (AUC) metrics. Results: A total of 1267 adolescents (13.1 %) were found to have high CBCL scores. The best performing algorithms were elastic net and gradient boosted trees. The best performing elastic net models included prenatal and family history factors (Sensitivity 0.654, Specificity 0.713; AUC 0.742, F1-score 0.401) and prenatal, family, history, and sociodemographic factors (Sensitivity 0.668, Specificity 0.704; AUC 0.745, F1-score 0.402). Across all 5 ML algorithms, family history factors (e.g., either parent had nervous breakdowns, trouble holding jobs/fights/police encounters, and counseling for mental issues) and sociodemographic covariates (e.g., maternal age, child's sex, caregiver income and caregiver education) tended to be better predictors of adolescent psychopathology. The most important prenatal predictors were unplanned pregnancy, birth complications, and pregnancy complications. Conclusion: Our results suggest that inclusion of prenatal, family history, and sociodemographic factors in ML models can generate moderately accurate predictions of adolescent psychopathology. Issues associated with model overfitting, hyperparameter tuning, and system seed setting should be considered throughout model training, testing, and validation. Future early risk predictions models may improve with the inclusion of additional relevant covariates.http://www.sciencedirect.com/science/article/pii/S2590113324000270AdolescentChildPregnancyMental disordersMachine learningRisk prediction |
| spellingShingle | Faizaan Siddique Brian K. Lee Predicting adolescent psychopathology from early life factors: A machine learning tutorial Global Epidemiology Adolescent Child Pregnancy Mental disorders Machine learning Risk prediction |
| title | Predicting adolescent psychopathology from early life factors: A machine learning tutorial |
| title_full | Predicting adolescent psychopathology from early life factors: A machine learning tutorial |
| title_fullStr | Predicting adolescent psychopathology from early life factors: A machine learning tutorial |
| title_full_unstemmed | Predicting adolescent psychopathology from early life factors: A machine learning tutorial |
| title_short | Predicting adolescent psychopathology from early life factors: A machine learning tutorial |
| title_sort | predicting adolescent psychopathology from early life factors a machine learning tutorial |
| topic | Adolescent Child Pregnancy Mental disorders Machine learning Risk prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2590113324000270 |
| work_keys_str_mv | AT faizaansiddique predictingadolescentpsychopathologyfromearlylifefactorsamachinelearningtutorial AT brianklee predictingadolescentpsychopathologyfromearlylifefactorsamachinelearningtutorial |