Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System

Abstract

The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed method was compared to recently proposed dental procedures. Among these classifiers, the suggested GBDT trained with a reduced feature set achieved the best classification performance, with accuracy, F1-score, precision, and recall values of 95%, 93%, 99%, and 88%, respectively. Furthermore, the experimental results suggest that feature selection improved the performance of the various classifiers. The suggested method yielded a good predictive model for dental caries diagnosis, which might be used in more imbalanced medical datasets to identify disease more effectively.

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

“…The dependent variable: Obstructive sleep apnea (OSA) is the dependent variable that corresponds to the 2015 015 ICD-9-CM Diagnosis Code 327. 23 Obstructive sleep apnea (adult) (pediatric). OSA is diagnosed using a combination of clinical evaluation and objective testing, such as polysomnography (PSG) [35].…”

“…Given these limitations, it is important to conduct large-scale research regarding dental status and OSA associations, that follow a strict protocol for dental and medical disease definitions and consider the presence of numerous confounding factors [ 23 ].…”

“…Moreover, whilst most research has been using only statistical methods to tackle the topic, machine learning (ML) methodologies in artificial intelligence have recently been utilized to choose the most important variables (namely the feature selection/feature importance) in the identification of caries [ 23 ], and periodontitis [ 24 ] including our publication which studied the associations between BMI and dental caries using ML and statistical models [ 25 ]. To the best of our knowledge, no previous research has been published using statistical and machine learning models to study the associations between OSA and dental status in the context of metabolic dysfunction.…”

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

“…Kang et al [ 27 ] conducted another study with the same dataset and used GBDT, RF, LR, SVM and long short-term memory algorithms; GBDT achieved the highest success, with an accuracy, F1-score, precision and recall of 95%, 93%, 99% and 88%, respectively. In this study, the DT model achieved 82% accuracy, 82% F1-score, 82% precision and 82% recall.…”