Title

Behavioral outcome prediction among children using machine learning

Authors

P.V. Samir^1,*, G Aruna Kumari², Nandini Biradar³, Kodali Srija², Debasmita Das⁴ & Sukabhogi Anusha⁵

Affiliation

¹Department of Pedodontics & Preventive Dentistry, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar- 751006, India; ²Department of Pedodontics & Preventive Dentistry, Malla Reddy Dental College for Women- Mallareddy Viswavidyapeeth, Hyderabad, India; ³Department of Conservative dentistry and Endodontics, Private Practitioner, Biradar Dental care, Bidar, Karnataka, India; ⁴Department of Pedodontics and Preventive Dentistry, Hi-Tech Dental College & Hospital, Bhubaneswar, India; ⁵Department of Pedodontics and Preventive Dentistry, Meghana Institute of Dental Sciences, Nizamabad- 503001, Telangana, India; *Corresponding author

Email

PV Samir - E-mail: pv.samir@kids.ac.in
G Aruna Kumari - E-mail: aruna2thdoc@gmail.com
Nandini Biradar - E-mail: dr.nandini.salger@gmail.com
Kodali Srija - E-mail: srijakodali5@gmail.com
Debasmita Das - E-mail: dasdebasmita08@gmail.com
Sukabhogi Anusha - E-mail: anushapatil.sukhabogi@gmail.com

Article Type

Research Article

Date

Received June 30, 2025; Revised June 30, 2025; Accepted June 30, 2025, Published June 30, 2025

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.

Keywords

Pediatric dentistry, machine learning, behavioral prediction, dental anxiety, random forest, frankl scale

Citation

Samir et al. Bioinformation 21(6): 1555-1558 (2025)

Edited by

Hiroj Bagde MDS, (PhD), PGDCR, PGDHHM, PGDL, PGDM

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.