Use of statistical models for predicting oral health status of children with cerebral palsy in Sri Lanka

Abstract

Cerebral Palsy (CP) is the most common movement disorder in children, which is defined as ‘‘a group of permanent disorders of the development of movement and posture, causing activity limitations attributed to non-progressive disturbances occurred in developing fetal or infant brain. In this study, we consider the four most common CP types categorized by the location of movement problems named Monoplegia, Diplegia, Hemiplegia, and Quadriplegia. Oral health is a state of being free from the chronic mouth, facial pain, oral and throat cancer, oral sores, congenital disabilities such as cleft lip and palate, tooth decay and tooth loss, and other diseases disorders oral cavity. The main goal of the study is to create suitable statistical models for predicting the oral health status of children with CP using Silness-Löe plaque index and DMFT Index (DMFTI). Also, to identify the relationships between DMFTI and demographic, DMFTI and CP location, Silness-Loe plaque index and demographic data, Silness-Loe plaque index and CP location, Care index (CI) and demographic data, and the CI and CP location. This analysis was performed on a sample of 93 children with CP in the Central Province, Sri Lanka. The independent sample t-test and one-way ANOVA test were used to identify the relationship between variables, and effect sizes were calculated using partial Eta squared value to measure the strength of the relationship. Further Multiple Linear Regression (MLR) model, Random Forest Regression (RFR) model, and the Support Vector Regression (SVR) model were used to predict the oral health status using DMFTI and plaque index separately. A comparison was conducted for the fitted models using the Coefficient of determination (R-squared). There is a significant difference between the mean values of the plaque index for different CP locations. Children with diplegia have the lowest plaque index, while children with hemiplegia have the highest plaque index. The accuracy of the MLR model for predicting DMFTI is 23.60% and 20.80% for Permanent and primary teeth separately, and 20.00% for predicting Plaque Index. Those accuracies for the RFR model are 92.64%, 93.11% and 90.32%, while 95.36%, 85.65% and 80.07% for SVR model respectively. Therefore, the RFR Model was considered the best-fitted model for predicting oral health status using DMFTI and the plaque index of Sri Lankan children with CP. Besides, children with hemiplegia have a higher risk of having lower oral health status.