Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.

Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach / Rothenberg, W. Andrew; Bizzego, Andrea; Esposito, Gianluca; Lansford, Jennifer E.; Al-Hassan, Suha M.; Bacchini, Dario; Bornstein, Marc H.; Chang, Lei; Deater-Deckard, Kirby; Di Giunta, Laura; Dodge, Kenneth A.; Gurdal, Sevtap; Liu, Qin; Long, Qian; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T.; Sorbring, Emma; Tapanya, Sombat; Steinberg, Laurence; Tirado, Liliana Maria Uribe; Yotanyamaneewong, Saengduean; Alampay, Liane Peña. - In: JOURNAL OF YOUTH AND ADOLESCENCE. - ISSN 0047-2891. - 52:8(2023), pp. 1595-1619. [10.1007/s10964-023-01767-w]

Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach

Bizzego, Andrea;Esposito, Gianluca;
2023-01-01

Abstract

Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.
2023
8
Rothenberg, W. Andrew; Bizzego, Andrea; Esposito, Gianluca; Lansford, Jennifer E.; Al-Hassan, Suha M.; Bacchini, Dario; Bornstein, Marc H.; Chang, Lei; Deater-Deckard, Kirby; Di Giunta, Laura; Dodge, Kenneth A.; Gurdal, Sevtap; Liu, Qin; Long, Qian; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T.; Sorbring, Emma; Tapanya, Sombat; Steinberg, Laurence; Tirado, Liliana Maria Uribe; Yotanyamaneewong, Saengduean; Alampay, Liane Peña
Predicting Adolescent Mental Health Outcomes Across Cultures: A Machine Learning Approach / Rothenberg, W. Andrew; Bizzego, Andrea; Esposito, Gianluca; Lansford, Jennifer E.; Al-Hassan, Suha M.; Bacchini, Dario; Bornstein, Marc H.; Chang, Lei; Deater-Deckard, Kirby; Di Giunta, Laura; Dodge, Kenneth A.; Gurdal, Sevtap; Liu, Qin; Long, Qian; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T.; Sorbring, Emma; Tapanya, Sombat; Steinberg, Laurence; Tirado, Liliana Maria Uribe; Yotanyamaneewong, Saengduean; Alampay, Liane Peña. - In: JOURNAL OF YOUTH AND ADOLESCENCE. - ISSN 0047-2891. - 52:8(2023), pp. 1595-1619. [10.1007/s10964-023-01767-w]
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