Decision trees and random forests

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Extraction decision and identification of treatment predictors in Class I malocclusions

Konstantonis, D., Anthopoulou, C., & Makou, M. (2013) · Progress in Orthodontics · doi:10.1186/2196-1042-14-47 · Cited by 95

The wisdom behind third molar extractions

Kandasamy, S., Rinchuse, D. J., & Rinchuse, D. (2009) · Australian Dental Journal · doi:10.1111/j.1834-7819.2009.01152.x · Cited by 74

Validation of machine learning algorithms

Burzykowski, T., Geubbelmans, M., Rousseau, A.-J., & Valkenborg, D. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.05.007 · Cited by 55

Supervised learning

Valkenborg, D., Geubbelmans, M., Rousseau, A.-J., & Burzykowski, T. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.04.010 · Cited by 30

Introduction to machine learning

Burzykowski, T., Rousseau, A.-J., Geubbelmans, M., & Valkenborg, D. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.02.005 · Cited by 27

Artificial neural networks and deep learning

Geubbelmans, M., Rousseau, A.-J., Burzykowski, T., & Valkenborg, D. (2024) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.11.003 · Cited by 22

High-dimensional data

Geubbelmans, M., Rousseau, A.-J., Valkenborg, D., & Burzykowski, T. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.06.012 · Cited by 3

A Decision Tree for Orthodontic and Surgical Management of the Maxillary Transverse Dimension in Orthognathic Surgery

Rasmussen, C. M., & Viozzi, C. F. (2020) · Journal of Oral and Maxillofacial Surgery · doi:10.1016/j.joms.2020.02.029 · Cited by 2

Multi-resolution ozone mapping over the Yangtze River Delta (2014–2022) using a data-driven spatial weighting and downscaling framework

Yang, N., Qian, J., Wang, J., Chen, J., Li, S., Wang, X. E., Tao, C., Wang, N., Jiang, M., Zhang, T., et al. (2026) · Atmospheric Pollution Research · doi:10.1016/j.apr.2026.103067

Prolonged P3 latency predicts clinical response to repetitive transcranial magnetic stimulation in tinnitus

Ding, Z.-L., Zhou, W.-C., Gong, M.-F., Liu, J.-S., Dai, Y., & Tao, D.-D. (2026) · Clinical Neurophysiology Practice · doi:10.1016/j.cnp.2026.05.003

Raman Spectroscopy of Protein–Polysaccharide Conjugates: A Comparative Study of Tree-Based Ensemble Models

Александровна, Ш. С., Grigoryan, S. A., Mayorova, O. A., Saveleva, М. S., & Сергеевна, П. Е. (2026) · Macromol—A Journal of Macromolecular Research · doi:10.3390/macromol6020037

A dual-marker approach for forensic age prediction: integrating the salivary microbiome and antibiotic resistome

Yu, D., Zhang, L., Zhang, L., Damu, A., Gao, N., Xu, J., Hao, J., Xiao, Y., Zhang, J., & Yan, J. (2026) · International Journal of Legal Medicine · doi:10.1007/s00414-026-03867-1

Early Fault Prediction in Industrial Equipment using MA-LSTM-CNN

Dinesh.T, Manogar.K, Prabu.M, & K.Chandrasekaran (2026) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn68523.2026.11543791

Modelling extraction-induced water table decline and potential relationship with arsenic mobilization in the Western Bengal Delta

Mahammad, S., Islam, A., Das, B. C., Sarkar, B., Islam, A. R. M. T., & Pal, S. C. (2026) · Theoretical and Applied Climatology · doi:10.1007/s00704-026-06320-w

A machine learning model for predicting pneumothorax risk after computed tomography-guided percutaneous transthoracic needle biopsy: A two-hospital retrospective study

Tian, F., Zhang, R., Yu, L., Xu, Y., Liu, H., & Yu, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-57793-8

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms

Ao, R., Zhan, P., Wang, G., Liu, H., & Wang, W. Y. (2026) · iScience · doi:10.1016/j.isci.2026.116408

Aspartame drives the continuous progression from MASLD to HCC

Zhang, X., Tan, R., Ma, Y., Zhang, J. F., Wang, Q., & Chen, B. (2026) · Discover Oncology · doi:10.1007/s12672-026-05503-1

A Community-Based Machine Learning Model for Predicting Major Adverse Cardiovascular Events in Kazakh Hypertensive PatientsPreprint

Shu, H., Yining, Y., Zhao, B., Hu, J., Zheng, Y., Yang, S., & Feng, X. (2026) · Research Square · doi:10.21203/rs.3.rs-9308614/v1

A two-phase machine learning framework for coronary heart disease risk prediction: Integrating population ecology with individual pathophysiology

Yang, L., Guo, J., & Xu, J. (2026) · Digital Health · doi:10.1177/20552076261466813

Predicting Life Expectancy in Upper‐Middle‐Income Countries Using Machine Learning: A Comparative Analysis

Süzen, E., & Kayakuş, M. (2026) · Sustainable Development · doi:10.1002/sd.71468

Crop Evapotranspiration Forecasting for IoT-Based Smart Irrigation via a Data Science Review of Statistical, Machine Learning and Deep Learning Methods

Wongjeam, S., Chuasuk, P., & Thongnim, P. (2026) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecti-con68836.2026.11608753

Development and validation of a nomogram for predicting hemoptysis risk in patients with bronchiectasis: A study based on Random Forest algorithm

Zhang, G., Chen, Y., Zhai, M., & Zhao, L. (2026) · Medicine · doi:10.1097/md.0000000000050019

Application of Ensemble Machine Learning Methods to the Classification and Interpretation of Raman Spectra of Adipose Tissue During Enzymatic Hydrolysis by Lipase

Сергеевна, П. Е. (2026) · Journal of Analytical Chemistry · doi:10.1134/s1061934826700516

Traffic Flow Prediction Based on Hypergraph Transformer: A Case Study in Huangmaohai Cross-Sea Corridor

Jiang, F., Ma, Z., Mukozomba, P., Ke, Z., Zhang, S., & Yu, H. (2026) · Applied Sciences · doi:10.3390/app16168216

Identification of Progressive Islet Biomarkers for Type 2 Diabetes by Integrated Transcriptomics and Mendelian Randomization: Alterations Spanning the Normal Glucose Tolerance–Impaired Glucose Tolerance–Type 2 Diabetes Continuum

Aisikaer, N., Liu, Z., & Cai, S. (2026) · Biomedicines · doi:10.3390/biomedicines14081850

Machine learning-based prediction model for postoperative blood transfusion in pediatric patients with Hirschsprung’s disease

Luo, J., Wei, Y., Zhou, R., Huang, K., Li, Q., & Li, L. (2026) · Pediatric Surgery International · doi:10.1007/s00383-026-06594-1

Metabolomics Reveals Metabolic Characteristics of Functional Cure in Chronic Hepatitis B Treated With Entecavir Combined With Pegylated Interferon Alpha

Wang, J., Kong, X., Xu, H., Zhang, W., & Peng, P. (2026) · BioMed Research International · doi:10.1155/bmri/1031453

Evaluating the thermal impacts of urban corridor redevelopment and greening interventions in Addis Ababa using multi-temporal satellite data and machine learning

Reda, T. M., Melese, A., Gebremariam, W. H., Chere, Z., & Alemayehu, B. (2026) · City and Environment Interactions · doi:10.1016/j.cacint.2026.100465

Research on the Relationship between Digital Economy and Regional Integrated Innovation Based on Machine Learning: Empirical Evidence from Random Forest and Panel Model

Li, C., & Li, X. (2026) · doi:10.1145/3838457.3838536

Random Forest-Based Analysis of Return Migration Drivers for Rural Young and Middle-aged Laborers in Hubei Province

Yin, S., & Wei, Y. (2026) · International Conference on Digital Society and Artificial Intelligence (DSAI) · doi:10.1145/3833541.3833634

Predicting the Willingness of Low-Income Groups to Participate in Flexible Employment Using Random Forest

Zhang, Y., Yang, S., & Li, Z. (2026) · International Conference on Digital Society and Artificial Intelligence (DSAI) · doi:10.1145/3833541.3833611

Development and Validation of a Machine Learning–Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study

Ding, W., Fang, L., & Fang, C. (2026) · JMIR Medical Informatics · doi:10.2196/85557

Detecting False Data Injection Attacks in Smart Grids

Rani, S. S., & Arnaud, M. (2026) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc70543.2026.11693876

Low-Cost Microcontroller Based Indoor Positioning

Simelane, T. Q., Mathaba, T. N. D., & Otunniyi, T. O. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-38806-3_18

New insights into tumor size in colorectal cancer: smaller size, higher risk?

Ding, R., Li, T., Li, C., Song, W., Gao, X., Xu, M., & Wang, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1908104

A Hybrid Feature Selection Framework for High-Dimensional Data Using Embedded and Deep Learning Techniques

Ajmal, C., & Sreeja.K.Soman (2026) · International Conference on Innovations in Mechanical, Robotics, Computing and Biomedical Engineering (ICIMRBE) · doi:10.1109/icimrbe70745.2026.11710062

Convolutional neural network for red blood cell transfusion prediction: A machine learning based study

Zheng, L., Ma, J., Deng, M., Ma, Y., Zhang, Y., Wang, L., & Li, X. (2026) · Transfusion Medicine · doi:10.1111/tme.70126

A demonstration of machine learning approaches and visualizations for HIV research: identifying key predictors of barriers to antiretroviral therapy

Chu, D., Schuster, T., Engler, K., Vicente, S., Lessard, D., Routy, J.-P., Kronfli, N., Cox, J., de Pokomandy, A., Palich, R., et al. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.101036

OmniFormer: A patch transformer for multi-asset multivariate cryptocurrency time series forecasting

Nguyen, T. N., Nguyen, A. Q., Son, H. X., Phien, N. N., Tuan, T. P. H., Le, A. N., & Chan, N. G. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116725

Multi-Cohort Transcriptomic Analysis Reveals Heterogeneous Ferroptosis-Related Transcriptional Programs Across EAOC-Associated Pathological States

Liu, Z., Liu, Y., Qiang, R., Zhu, Y., Chen, Y., He, Q., Fang, J., Zhou, X., & Wang, Y. (2026) · Biomedicines · doi:10.3390/biomedicines14112436

Harnessing Clinical Characteristics to Identify High-Risk of Drug-induced QTc Prolongation in Hospitalized Patients

Zhao, Y., Weng, B.-B., Lan, G., Jin, N.-S., Zhang, Z., Lai, X., Liu, Y., & Wang, Q. (2026) · Clinical Medicine · doi:10.1016/j.clinme.2026.100666

Feature Correlation Transformer for Estimating Ambiguous Optical Flow

Fang, G., Chen, J., Liang, D., Asim, M., Reeth, F. V., Claesen, L., Yang, Z., & Liu, W. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11273-6 · Cited by 1

CrossFlow: Learning cost volumes for optical flow by cross-matching local and non-local image features

Liu, Z., Liu, Z., Wu, X., Chen, W., Liu, Z., & Li, Z. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104588 · Cited by 2

Crossflow: Learning Better Cost Volumes for Optical Flow by Crossing Non-Local and Local InformationPreprint

Liu, Z., Liu, Z., Wu, X., Chen, W., Liu, Z., & Li, Z. G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4777517

Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation

Zhang, F., Woodford, O. J., Prisacariu, V. A., & Torr, P. H. S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01063 · Cited by 110

CGCV:Context Guided Correlation Volume for Optical Flow Neural NetworksPreprint

Li, J., & Niu, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.10174