Machine learning methods applied on dental fear and behavior management problems in children.

Area of Science:

• Pediatric Dentistry
• Artificial Intelligence in Healthcare
• Psychology

Background:

• Dental fear and behavior management problems are significant challenges in pediatric dentistry.
• Understanding their distinct etiologies is crucial for effective intervention.

Purpose of the Study:

• To investigate the underlying causes of dental fear and behavior management problems in children.
• To differentiate the contributing factors for each phenomenon.

Main Methods:

• Utilized a large database of 2,257 Swedish children aged 4-11 years.
• Employed artificial intelligence-based inductive techniques (XpertRule Analyser) to analyze complex relationships.
• Analyzed both numerical and discrete variables, including dental health, treatment history, parental dental fear, general anxiety, and socioeconomic factors.

Main Results:

• Inductive methods proved effective for analyzing multifactorial relationships in large datasets.
• Knowledge trees revealed distinct patterns for dental fear and behavior management problems, indicating they are not equivalent.
• Dental fear was more strongly associated with non-dental variables (e.g., general anxiety).
• Behavior management problems were more closely linked to dental variables (e.g., dental health, treatments).

Conclusions:

• Dental fear and behavior management problems in children have different etiological pathways.
• Non-dental factors play a more significant role in pediatric dental fear.
• Dental-specific factors are more influential in children's behavior management during dental visits.