Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System
Authors
Keywords
-
Journal
Bioengineering
Volume 10, Issue 2, Pages 245
Publisher
MDPI AG
Online
2023-02-13
DOI
10.3390/bioengineering10020245
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Feature Selection With Maximal Relevance and Minimal Supervised Redundancy
- (2022) Yadi Wang et al. IEEE Transactions on Cybernetics
- Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiographs
- (2022) Mohamed Estai et al. Oral Surgery Oral Medicine Oral Pathology Oral Radiology
- Graph-based relevancy-redundancy gene selection method for cancer diagnosis
- (2022) Saeid Azadifar et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Fuzzy parameterized fuzzy soft k-nearest neighbor classifier
- (2022) S. Memiş et al. NEUROCOMPUTING
- Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with application in gene selection
- (2022) Farid Saberi-Movahed et al. KNOWLEDGE-BASED SYSTEMS
- A novel random multi-subspace based ReliefF for feature selection
- (2022) Baoshuang Zhang et al. KNOWLEDGE-BASED SYSTEMS
- Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method
- (2021) Simin Li et al. NEURAL COMPUTING & APPLICATIONS
- Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system
- (2021) Celestine Iwendi et al. MULTIMEDIA SYSTEMS
- Deep learning for early dental caries detection in bitewing radiographs
- (2021) Shinae Lee et al. Scientific Reports
- Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms
- (2021) You-Hyun Park et al. International Journal of Environmental Research and Public Health
- A Study on Facial Expression Change Detection Using Machine Learning Methods with Feature Selection Technique
- (2021) Sang-Ha Sung et al. Mathematics
- A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices
- (2021) Samet Memiş et al. SOFT COMPUTING
- Predictive model of cooling load for ice storage air-conditioning system by using GBDT
- (2021) Wanhu Zhang et al. Energy Reports
- Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms
- (2020) Weizhang Liang et al. Mathematics
- Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
- (2020) Daniel Asante Otchere et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Development and evaluation of deep learning for screening dental caries from oral photographs
- (2020) Xuan Zhang et al. ORAL DISEASES
- A feature selection algorithm of decision tree based on feature weight
- (2020) HongFang Zhou et al. EXPERT SYSTEMS WITH APPLICATIONS
- A machine learning model for predicting the minimum miscibility pressure of CO2 and crude oil system based on a support vector machine algorithm approach
- (2020) Hao Chen et al. FUEL
- Investigating the impact of data normalization on classification performance
- (2019) Dalwinder Singh et al. APPLIED SOFT COMPUTING
- A review of feature selection methods in medical applications
- (2019) Beatriz Remeseiro et al. COMPUTERS IN BIOLOGY AND MEDICINE
- On some aspects of minimum redundancy maximum relevance feature selection
- (2019) Peter Bugata et al. Science China-Information Sciences
- Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm
- (2018) Jae-Hong Lee et al. JOURNAL OF DENTISTRY
- Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)
- (2018) Jiangkuan Xing et al. RENEWABLE ENERGY
- Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease
- (2006) Imran Kurt et al. EXPERT SYSTEMS WITH APPLICATIONS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started