Machine learning techniques for dental disease prediction

Rimi, Iffat Firozy; Arif, Md. Ariful Islam; Akter, Sharmin; Rahman, Md. Riazur; Islam, Ashraful; Habib, Md. Tarek

doi:10.1007/s42044-022-00101-0

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Hasil Pengklasifikasi regresi logistik mengungguli setiap pengklasifikasi lain dalam semua pengukuran dengan akurasi mendekati 95,89%. (Rimi et al, 2022).…”

Section: A Klasifikasiunclassified

Application of Machine Learning With the Binary Decision Tree Model in Determining the Classification of Dental Disease

Ula

Anjani

Ulva

et al. 2022

JITE

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The dangers of health problems in dental disease are common for children and adults. Many dental problems get priority treatment based on data from Riskesdas, about 67.6% of the Indonesian population suffers from dental and oral problems. This affects other parts of the organ that are interrelated. Therefore, this study formulates how to solve the determination of dental disease, by applying the UDB model in machine learning. The purpose of this study was to determine the application of machine learning Binary Decision Tree (BDT) in the classification of classified dental diseases identified by decision trees in determining the results of dental disease predictions including groups and how to solve them. The research methodology in the first stage of data collection was carried out directly with the dental clinic at Cut Meutia Lhokseumawe Hospital. Then input the dental disease data along with the dental disease symptom data. The final stage is dividing the attribute values in viewing the value at a predetermined branch which is then in the form of a decision tree as a reference for the final prediction. The results of the assessment have each value indicating a high level of accuracy, with an accuracy of 92 percent and an inaccuracy of 8 percent of the 40 data points tested. Furthermore, the conclusion of this study can produce an appropriate classification of dental disease and is able to produce accurate results seen from a small error rate

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“…Hasil Pengklasifikasi regresi logistik mengungguli setiap pengklasifikasi lain dalam semua pengukuran dengan akurasi mendekati 95,89%. (Rimi et al, 2022).…”

Section: A Klasifikasiunclassified

Application of Machine Learning With the Binary Decision Tree Model in Determining the Classification of Dental Disease

Ula

Anjani

Ulva

et al. 2022

JITE

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“…Recent research on dental caries was conducted by Rimi et al [3], who discussed the prediction of dental caries using Machine Learning(ML). In their experiments, they used nine algorithms, namely K-Nearest Neighbors (KNN), logistic regression (LR), support vector machine (SVM), random forest (RF), naïve Bayes (NB), classification and regression trees, multilayer perception (MLP), linear discriminant analysis (LDA), and adaptive Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Introductionmentioning

confidence: 99%

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

Kang¹,

Kim²

2023

Preprint

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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.

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A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities

Zanini,

Rubira-Bullen,

Nunes

2024

J Digit Imaging. Inform. med.

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Machine learning techniques for dental disease prediction

Cited by 7 publications

References 13 publications

Application of Machine Learning With the Binary Decision Tree Model in Determining the Classification of Dental Disease

Application of Machine Learning With the Binary Decision Tree Model in Determining the Classification of Dental Disease

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

A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities

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