# Development of artificial neural network model for predicting the rapid maxillary expansion technique in children with cleft lip and palate

## TL;DR

This study developed an artificial neural network model to predict the best rapid maxillary expansion technique for children with cleft lip and palate.

## Contribution

A hybrid biometric model combining bootstrap and logistic regression with neural network validation is proposed for RME prediction.

## Key findings

- A binary logistic regression model was developed to predict RME techniques based on factors like age, gender, and MPS.
- The hybrid model showed good accuracy in predicting the binary response variable for RME techniques.
- MLFFNN validation confirmed the precision of the generated model for both cleft and non-cleft individuals.

## Abstract

The study aimed to determine the mid-palatal suture (MPS) maturation stages and to develop a binary logistic regression model to predict the possibility of surgical or non-surgical rapid maxillary expansion (RME) in children with unilateral cleft lip and palate (UCLP).

A retrospective case control study was conducted. A total of 100 subjects were included. Data was gathered from the databases of Hospital Universiti Sains Malaysia and Hospital Raja Perempuan Zainab II, respectively. Cone beam computed tomography scans of both cleft and non-cleft individuals were utilized to determine the MPS maturation stages. Romexis software version 3.8.2 was used to analyze the images.

The results of the binary logistic regression model were utilized to establish the relationship between the probability (P) of a specific event of interest (P(Y = 1)) and a linear combination of independent variables (Xs) using the logit link function. Potential factors such as age, gender, cleft, category of malocclusion, and MPS were chosen which could play a role in predicting the technique of RME in children with UCLP and non-UCLP. A subset of these variables was validated via multilayer feed forward neural network (MLFFNN).

The effectiveness of the hybrid biometric model created in this work, which combines bootstrap and BLR with R-syntax was evaluated in terms of how accurately it predicted a binary response variable. A validation method based on an MLFFNN was used to evaluate the precision of the generated model. This leads to a good outcome.