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Detection of child depression using machine learning methods
Umme Marzia Haque1, Enamul Kabir1, Rasheda Khanam2
1School of Sciences, University of Southern Queensland, Toowoomba, Australia.
Insights
Machine learning accurately predicts depression in children and adolescents using the Young Minds Matter dataset. A Random Forest model achieved 99% accuracy, identifying key symptoms for early diagnosis.
Area of Science:
- Child and Adolescent Psychiatry
- Machine Learning in Healthcare
- Mental Health Research
Background:
- Childhood depression has severe, long-term impacts on individuals, families, and society.
- Early detection and accurate diagnosis are crucial to prevent severe consequences.
- No prior research has utilized machine learning on a high-prediction dataset like Young Minds Matter for this purpose.
Purpose of the Study:
- Develop a machine learning model to predict depression in children and adolescents (ages 4-17).
- Evaluate and compare the performance of various machine learning algorithms.
- Identify family activities and socioeconomic factors contributing to depression.
Main Methods:
- Utilized the Young Minds Matter (YMM) dataset (Australian Child and Adolescent Survey of Mental Health and Wellbeing 2013-14).
- Employed Boruta algorithm with Random Forest (RF) for feature selection.
- Used Tree-based Pipeline Optimization Tool (TPOT) to select models, including RF, XGBoost, Decision Tree, and Gaussian Naive Bayes.
Main Results:
- Identified 11 key features for depression detection, including mood, interest, sleep, and concentration changes.
- The Random Forest model achieved 99% accuracy and 99% precision in predicting depression.
- The RF model demonstrated superior performance across all metrics and a rapid execution time of 315ms.
Conclusions:
- The developed Random Forest model offers a highly accurate and informative approach to predicting child and adolescent depression.
- This model outperforms other evaluated algorithms in predictive accuracy and efficiency.
- The findings highlight the potential of machine learning for early identification and intervention in pediatric mental health.
Background:
Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate signs to anticipate mental illness as depression in children and adolescents is vital in making an early and accurate diagnosis to avoid severe consequences in the future. There has been no research employing machine learning (ML) approaches for depression detection among children and adolescents aged 4-17 years in a precisely constructed high prediction dataset, such as Young Minds Matter (YMM). As a result, our objective is to 1) create a model that can predict depression in children and adolescents aged 4-17 years old, 2) evaluate the results of ML algorithms to determine which one outperforms the others and 3) associate with the related issues of family activities and socioeconomic difficulties that contribute to depression.
Methods:
The YMM, the second Australian Child and Adolescent Survey of Mental Health and Wellbeing 2013-14 has been used as data source in this research. The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. The Tree-based Pipeline Optimization Tool (TPOTclassifier) has been used to choose suitable supervised learning models. In the depression detection step, RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been used.
Results:
Unhappy, nothing fun, irritable mood, diminished interest, weight loss/gain, insomnia or hypersomnia, psychomotor agitation or retardation, fatigue, thinking or concentration problems or indecisiveness, suicide attempt or plan, presence of any of these five symptoms have been identified as 11 important features to detect depression among children and adolescents. Although model performance varied somewhat, RF outperformed all other algorithms in predicting depressed classes by 99% with 95% accuracy rate and 99% precision rate in 315 milliseconds (ms).
Conclusion:
This RF-based prediction model is more accurate and informative in predicting child and adolescent depression that outperforms in all four confusion matrix performance measures as well as execution duration.
