Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

ABSTRACT:

Background

The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary.

Objectives

The main aim of this research was the establishment of machine learning models to correctly classify individual Arab patients, being citizens of Israel, as skeletal class II or III. Secondary outcomes of the study included comparing cephalometric parameters between patients with skeletal class II and III and between age and gender-specific subgroups, an analysis of the correlation of various cephalometric variables, and principal component analysis in skelet

SUBMITTER: Midlej K 

PROVIDER: S-EPMC11369042 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature