Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 16 January 2023 / Approved: 17 January 2023 / Online: 17 January 2023 (09:25:45 CET)

A peer-reviewed article of this Preprint also exists.

Kang, I.-A.; Njimbouom, S.N.; Kim, J.-D. Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System. Bioengineering 2023, 10, 245. Kang, I.-A.; Njimbouom, S.N.; Kim, J.-D. Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System. Bioengineering 2023, 10, 245.

Abstract

Caries is a prevalent oral disease that primarily affects children and teenagers. Advances in ma-chine learning have caught the attention of scientists working with decision support systems to predict early tooth decay. Current research has developed machine learning algorithm for caries classification and reached high accuracy especially in ML for image data. Unfortunately, most studies on dental caries only focus on classification and prediction tasks, meanwhile dental carries prevention is more important. Therefore, this study aims to design an efficient feature for decision support system machine learning based that can identify various risk factors that cause dental caries and its prevention. The data used in the research work was obtained from the 2018 Korean Children's Oral Health Survey, which totaled nine datasets. The experimental results show that combining the mRMR and GINI Feature Importance methods when training with the GBDT model achieved the optimum performance of 95%, 93%, 99%, and 88% for accuracy, F1 score, precision, and recall, respectively. So, the proposed method has provided effective predictive model for dental caries prediction.

Keywords

Disease Dental Caries; Gradient Boosting Decision Tree; Feature Selection; Machine Learning; Feature importance

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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