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Updated: Jan 17, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Behavioral outcome prediction among children using machine learning
Samir P V1, Aruna Kumari G2, Nandini Biradar3
1Department of Pedodontics & Preventive Dentistry, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar-751006, India.
Abstract:
Behavioural management in paediatric dentistry is essential for treatment success, yet predicting a child's behavior remains a challenge. This study used machine learning models on data from 120 children aged 4-10 years, incorporating clinical and historical variables such as age, dental history and parental anxiety. Among the models tested, Random Forest achieved the highest accuracy (87.5%) in predicting behavior based on the Frankl scale. Key predictors of negative behavior included younger age, high parental anxiety and prior negative dental experiences. These findings highlight the potential of machine learning to support behavior guidance planning and improve clinical outcomes.
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