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

An intelligent matching method for the equivalent circuit of electrochemical impedance spectroscopy based on Random Forest

Chen, W., Yan, B., Xu, A., Mu, X., Zhou, X., Jiang, M., Wang, C., Li, R., Huang, J., & Dong, J. (2024) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2024.05.024 · Cited by 37

Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning

Shen, M., Zhang, Y., Zhan, R., Du, T., Shen, P., Lu, X., Liu, S., Guo, R., & Shen, X. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117570 · Cited by 26

RNA splicing analysis deciphers developmental hierarchies and reveals therapeutic targets in adult glioma

Song, X., Tiek, D. M., Miki, S., Huang, T., Lu, M., Goenka, A., Iglesia, R. P., Yu, X., Wu, R., Walker, M. N., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci173789 · Cited by 25

Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis

Zhang, Z., Cheng, H., Chen, M., Zhang, L., Cheng, Y., Geng, W., & Guan, J. (2024) · Foods · doi:10.3390/foods13233956 · Cited by 23

Machine learning approaches for groundwater vulnerability assessment in arid environments: Enhancing DRASTIC with ANN and Random Forest

Baalousha, H. M. (2025) · Groundwater for Sustainable Development · doi:10.1016/j.gsd.2025.101496 · Cited by 21

Accelerated discovery and formation mechanism of high-entropy carbide ceramics using machine learning based on low-cost descriptors

Guo, W., Wang, L., Zhu, L., Ye, Y., Zhang, Z., Yang, B., Zhang, S., & Bai, S. (2024) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2024.175929 · Cited by 19

A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning

Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24

Soil organic carbon mediates plant immunity-rhizosphere microbiome interactions and controls colonization resistance to microbial inoculants

Huang, Y., Tang, S., Liu, R., Yu, P., Liu, J., Xiao, T., Zhang, Y., Fan, M., Zhang, F. S., & Ni, B. (2025) · Cell Host & Microbe · doi:10.1016/j.chom.2025.10.002 · Cited by 18

Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers

Onigbinde, S. B., Reyes, C. D. G., Sandilya, V., Chukwubueze, F. M., Oluokun, O. O., Sahioun, S., Oluokun, A., & Mechref, Y. S. (2024) · Expert Review of Proteomics · doi:10.1080/14789450.2024.2418491 · Cited by 17

Explainable artificial intelligence

Rousseau, A.-J., Geubbelmans, M., Valkenborg, D., & Burzykowski, T. (2024) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2024.01.006 · Cited by 21

Machine learning models predicts risk of proliferative lupus nephritis

Yang, P., Liu, Z., Lu, F., Sha, Y., Li, P., Zheng, Q., Wang, K., Zhou, X., Zeng, X., & Wu, Y. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1413569 · Cited by 15

Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm

Fan, X., Ye, R., Gao, Y., Xue, K., Zhang, Z., Xu, J., Zhao, J., Feng, J. H., & Wang, Y. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1473837 · Cited by 13

Development of a respiratory virus risk model with environmental data based on interpretable machine learning methods

Shi, S., Lin, H., Jiang, L., Zeng, Z., Lin, C., Li, P., Li, Y., & Yang, Z. (2025) · npj Climate and Atmospheric Science · doi:10.1038/s41612-025-00894-4 · Cited by 11

Swimming into the future: Machine learning in zebrafish behavioral research

Fontana, B. D., Canzian, J., & Rosemberg, D. B. (2025) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2025.111398 · Cited by 11

When the whole is greater than the sum of its parts: why machine learning and conventional statistics are complementary for predicting future health outcomes

Janse, R. J., Abu–Hanna, A., Vagliano, I., Stel, V. S., Jager, K. J., Tripepi, G. L., Zoccali, C., Dekker, F. W., & van Diepen, M. (2025) · Clinical Kidney Journal · doi:10.1093/ckj/sfaf059 · Cited by 12

Authenticity Identification and Quantitative Analysis of Dendrobium officinale Based on Near-Infrared Spectroscopy Combined with Chemometrics

Fan, Z., Li, Q., Zhang, Z.-T., Bai, L., Pu, X., Shi, T.-W., & Chai, Y. (2026) · Foods · doi:10.3390/foods15010121 · Cited by 9

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia

Wei, J., Cao, H., Peng, M., Zhang, Y., Li, S., Ma, W., & Li, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316526 · Cited by 8

Integration of multi-omics profiling reveals an epigenetic-based molecular classification of lung adenocarcinoma: implications for drug sensitivity and immunotherapy response prediction

Wang, N., Li, Y., Wang, Y., & Wang, W. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1540477 · Cited by 8

Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects

Li, C., Liu, J., Fu, P., & Zou, J. (2025) · Kidney Diseases · doi:10.1159/000546397 · Cited by 9

Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks

Jainonthee, C., Sanwisate, P., Sivapirunthep, P., Chaosap, C., Mektrirat, R., Chadsuthi, S., & Punyapornwithaya, V. (2024) · Poultry Science · doi:10.1016/j.psj.2024.104648 · Cited by 8

Evaluation of drug-drug interaction between Suraxavir Marboxil (GP681) and itraconazole, and assessment of the impact of gene polymorphism

Han, M., Cui, G., Zhao, Y., Zuo, X., Wang, X., Zhang, X., Mi, N., Jin, J., Xiao, C., Wang, J., et al. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1505557 · Cited by 7

Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation

Wu, W., Zhang, Z., Wang, S., Xin, R., Yang, D., Yao, W., Hei, Z., Chen, C., & Luo, G. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1548131 · Cited by 7

Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.

Hojati, M., Naderi, R., Edalat, M., & Pourghasemi, H. R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322442 · Cited by 8

Entropy-Driven Nucleic Acid Amplifier Based on Spatial Confinement as a “Booster” for Detection of Extracellular Vesicle MicroRNAs to Diagnose Gastric Cancer and Monitor Therapeutic Response

Xia, Y., Guo, L., Huang, Z., Li, X., Liu, X., Zeng, S., Fan, Y., & Yin, J. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05375 · Cited by 7

Explainable artificial intelligence for cross domain evaluation of predictive models in multi-disease diagnosis

Ansari, Z. A., Kumar, K. K., Fatima, S., Siddiqui, S., & Mohsin, S. W. (2026) · Discover Computing · doi:10.1007/s10791-026-10061-9 · Cited by 12

The influence of obesity on IOS parameters in asthma, COPD, and other lung diseases: analyzed by random forest

Ringbæk, T., Froelund, L., Mortensen, J., & Ali, H. H. E. (2025) · BMC Pulmonary Medicine · doi:10.1186/s12890-025-03619-8 · Cited by 6

Mapping the digital innovation ecosystem with machine learning: efficiency, drivers, and regional heterogeneity across China's provinces

Li, L., Meng, Z., Zhao, W., & Du, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104269 · Cited by 6

Comprehensive identification of immune-related biomarkers and therapeutic targets in preeclampsia: integrative bioinformatics and experimental validation

Wu, X., Li, X., Wu, Y., Yang, H., Wu, J., & He, L. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-08169-9 · Cited by 6

Peak alpha frequency as an objective biomarker for cognitive assessment in post-stroke cognitive impairment

Zhao, Y., Shi, H., Kong, W., Wang, X., Wei, W., Zhan, Z., & Xue, X. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1639970 · Cited by 6

On-site measurement and machine learning prediction of age-specific outdoor thermal and humidity comfort in urban microclimates in summer

Ren, J., Wang, J., Mao, Y., Cao, X., & Kong, X. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114199 · Cited by 6

Ensemble Classification Model With CFS‐IGWO–Based Feature Selection for Cancer Detection Using Microarray Data

Panda, P., Bisoy, S. K., Kautish, S., Ahmad, R., Irshad, A., & Sarwar, N. (2024) · International Journal of Telemedicine and Applications · doi:10.1155/2024/4105224 · Cited by 5

An artificial intelligence model integrating culprit lesion diagnosis and risk assessment for acute coronary syndrome

Xu, P., Hu, B., Zhou, F., Xu, Z. F., Chen, Q., Liu, T. Y., Guo, B., Zhou, C. S., Tao, X. W., Qiao, H. Y., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103415 · Cited by 6

Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities

Hu, Y., Gao, C., Wang, Y., Wen, Z., Yang, C., Deng, H., Chen, S., Li, Y., Pang, H., Zhou, P., et al. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1581422 · Cited by 5

Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock

Lyu, P., Xie, N., Shao, X.-P., Xing, S., Wang, X., Duan, L.-Y., Zhao, X. F., Lu, J., Liu, R.-F., Zhang, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-95028-4 · Cited by 5

Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics

Xie, X., Wu, C., Huang, Z., Zhou, Y., Huang, J., Dao, F., Yan, D., Deng, K., Lyu, H., Ma, C., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag083 · Cited by 4

Air passenger mode choice modelling for airport access: a review

Swami, L., Ahmed, M. A., & Jena, S. (2025) · Innovative Infrastructure Solutions · doi:10.1007/s41062-025-02099-3 · Cited by 4

Predicting the effects of temperature variability on nutritional status of children under five in Sub-Saharan Africa using machine learning

Bachwenkizi, J., He, C., Zhu, Y., Mugisha, A., Tlou, B., Moshiro, C. S., Mwambi, H. G., Madzorera, I., Chen, R., Kan, H., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-39659-1 · Cited by 4

Rapid and non-destructive identification of resistance levels to Phomopsis seed decay in soybean via near-infrared spectroscopy and machine learning algorithms

Lv, M., Wang, M., Xiu, J., Xv, M., Zhang, D., Tao, S., Gao, S., Gao, H., Zhang, X., Wang, X., et al. (2026) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2026.109043 · Cited by 4

Integrative multi-omics and machine learning reveals the spatial niche distribution and role of CYP27A1+TAMs in immunotherapy response in non-small cell lung cancer

Liu, Q., Liu, X., Zhang, H., Jiang, Y., Shi, Y., Mu, Q., Jing, Y., & Sun, D. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1782545 · Cited by 4

A novel paradigm on data and knowledge-driven drug formulation development: Opportunities and challenges of machine learning

Wang, X., Liu, Z., Lin, X., Hong, Y., Shen, L., & Zhao, L. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100796 · Cited by 4

Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics

Pan, M., Lu, L., Mu, X., & Jin, G. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1539574 · Cited by 4

Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm

Zhang, C., Yu, L., Li, L., Zeng, P., & Zhang, X. (2024) · Sleep And Breathing · doi:10.1007/s11325-024-03151-9 · Cited by 4

Uncovering Key Features for Predicting Comorbid Chronic Eosinophilic Pneumonia in Chronic Rhinosinusitis via Machine Learning

Ishikawa, M., Jiang, Z., Nguyen, C. H., Hatsukawa, H., Hirai, T., Matsumoto, H., Saito, E., Okazaki, K., Endo, K., Terada, S., et al. (2025) · International Forum of Allergy & Rhinology · doi:10.1002/alr.23613 · Cited by 4

Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods

Wang, H.-W., Huang, Y.-C., Fang, Y., Jang, T.-N., Chen, M., & Tsai, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-97787-6 · Cited by 2

Development and validation of an improved volumetric breast density estimation model using the ResNet technique

Asai, Y., Yamamuro, M., Yamada, T., Kimura, Y., Ishii, K., Nakamura, Y., Otsuka, Y., & Kondo, Y. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/adecac · Cited by 2

Exploring the Impact of Urinary Metal Exposure on Blood Lipid Profiles in Chinese Adults: Mediating Roles of Health‐Related Indicators

Zhong, J., Xu, Z., Yang, T., Liu, L., Zhang, R., Wang, Y., Huang, L., & Hong, F. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.125.042597 · Cited by 2

Predicting deep vein thrombosis using machine learning and blood routine analysis

Su, J., Tang, Y., Wang, Y., Chen, C., & Song, B. (2025) · Frontiers in Big Data · doi:10.3389/fdata.2025.1605258 · Cited by 2

Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort

Ding, N., Li, Y., Zhao, Z. M., Meng, X., Sun, M., Ren, X., & Wang, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1667794 · Cited by 2

Integrating Genome-Wide Association Study (GWAS) and Marker-Assisted Selection for Enhanced Predictive Performance of Soybean Cold Tolerance

Xue, Y. J., Tang, X., Zhu, X., Zhang, R., Yao, Y., Cao, D., He, W., Liu, Q., Luan, X., Shu, Y., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms27010165 · Cited by 2

Interpretability ML and AI accelerate the computation of fretting wear behaviours and parameter response of corroded copper-magnesium alloy for the prediction model

Rong, B., Li, B., Ding, S.-Y., Zhou, Y., Tang, Y.-J., Peng, J., & Zhu, M.-H. (2026) · Wear · doi:10.1016/j.wear.2025.206434 · Cited by 2

Comparison of machine learning algorithms for predicting cognitive impairment using neuropsychological tests

Simfukwe, C., An, S. S. A., & Youn, Y. C. (2024) · Applied Neuropsychology Adult · doi:10.1080/23279095.2024.2392282 · Cited by 4

Combining polynomial regression with unsupervised machine learning on wafer-level packaging reliability prediction

Liao, H. H., Su, Q., & Chiang, K. (2024) · Journal of Mechanics · doi:10.1093/jom/ufae042 · Cited by 2

Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms

An, Y., Sun, Y., Feng, S., Wang, Y.-S., Chen, Y., & Jiang, J. (2025) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v17.i10.111163 · Cited by 2

Elementomics of 32 elements in cord serum depicts the risk of orofacial clefts: A case-control study in Shanxi, China

Wei, J., Fu, D., Guo, S., Tian, T., Huang, Y., Li, Z., Wang, L., Jin, L., Ye, W., Ren, A., et al. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.125037 · Cited by 1

Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning

Majid, I., & Sergeev, Y. V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252111649 · Cited by 1

Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry.

Cagna, D. R., Donovan, T. E., McKee, J., Metz, J., Marzola, R., Murphy, K. G., & Troeltzsch, M. (2024) · The Journal of prosthetic dentistry (Print) · doi:10.1016/j.prosdent.2024.10.014 · Cited by 4

Predictive model for PSA persistence after radical prostatectomy using machine learning algorithms

Du, H., Wang, G., Yan, Y., Li, S., & Yang, X. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1452265 · Cited by 1

KNN algorithm for accurate identification of IFP lesions in the knee joint: a multimodal MRI study

Zhou, P., Liu, Z., Dai, J., Yang, M., Sui, H., Huang, Z., Li, Y., & Song, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-02786-2 · Cited by 1

Monitoring Mangrove Forest Changes and Estimating Carbon Stock in Bac Lieu, Vietnam From 2014 to 2023 by Using Remote Sensing

Nguyen, H. M., Vo, T. P. L., & Thanh, V. Q. (2025) · International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) · doi:10.1109/iccmso67468.2025.00073 · Cited by 1

Mendelian randomisation and single-cell transcriptomic analyses reveal serotonin promotes multiple sclerosis progression by suppressing adenosine deaminase activity

Huang, L., Shi, J., Li, H., & Lin, Q. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-102876 · Cited by 1

Exploring the Impact of E-Waste Exposure on Childhood Blood Pressure: Metabolomics Analysis and Risk Prediction

Li, M., Chen, T.-H., Liu, Y., Xiang, M., Kuang, H., & Yu, Y.-J. (2025) · Environment & Health · doi:10.1021/envhealth.5c00189 · Cited by 1

Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis

Temirbayeva, A. B., & Altybay, A. (2025) · RUDN Journal of Engineering Researches · doi:10.22363/2312-8143-2025-26-2-168-180 · Cited by 1

A flexible microfiber tactile wearable sensor inspired by the microstructure of fingertip skin for human pulse monitoring

Zou, Y., Liu, M., Mao, J., Yu, Y., Liu, H., Guo, S., Sun, D., & Ma, J. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109226 · Cited by 1

Characterization of active components in perilla frutescens using excitation-emission matrix fluorescence coupled with parallel factor analysis for content prediction

Ge, Z., Di, Y., Yin, Y., Yuan, Y., Yu, H., & Chen, J. (2025) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-025-03760-2 · Cited by 1

The role of cognitive function in predicting metabolic risk in schizophrenia: a multi-model comparison incorporating clinical features

Li, R., Ren, X., Jiang, T., Huo, J., Ping, J., Zhu, S., & Luo, A. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1724238 · Cited by 1

Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction

Gan, Y., Song, S., Zhang, D., Chen, F., He, J., Guan, Z., Tu, K., Yu, Z., Pan, Y., Miao, Z., et al. (2026) · Stroke and Vascular Neurology · doi:10.1136/svn-2025-004258 · Cited by 1

Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives

Ye, J., Gao, M., Jia, M., Li, J., Duan, G., Hou, L., Yuan, C., & Liu, Z. (2026) · Advanced Functional Materials · doi:10.1002/adfm.77567 · Cited by 1

Boosting

Becker, T., Geubbelmans, M., Rousseau, A.-J., Valkenborg, D., & Burzykowski, T. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.10.003

Machine learning Algorithm Predicting Deep Vein Thrombosis Based on Blood Routine AnalysisPreprint

Su, J., Tang, Y., Wang, Y., Chen, C., & Song, B. (2024) · Research Square · doi:10.21203/rs.3.rs-4385977/v1

Bioinformatic Analysis of Collagen Sequences

Xiao, J. (2024) · Springer series in biomaterials science and engineering · doi:10.1007/978-981-10-1941-8_3

Combing Wildfire Danger Index and Geo-Kompsat-2A Geostationary Meteorological Satellite Data for Wildfire Detection

Wang, Z., Hou, J., & Yi, X. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10641287

Research on Momentum Dynamic Prediction Based on BP Neural Network and Support Vector Machine Algorithm

Yu, W., Wan, H., & Yang, Z. (2024) · International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII) · doi:10.1109/icmiii62623.2024.00112

Research on Nonlinear Programming Problems Based on Random Forest Model and Genetic Algorithm

Li, Y., Qing, Y., Huang, W., Qi, K., Du, K., & Li, G. (2024) · Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1109/ipec61310.2024.00120

An Integrated Learning Prediction Study of Diabetes Mellitus Based on Random Forest and Support Vector Machine

Huang, S., & Han, X. (2024) · International Conference on Computers, Information Processing and Advanced Education (CIPAE) · doi:10.1109/cipae64326.2024.00070

Machine Learning Based Prediction Model of Recovery Rate for Water-Driven Gas Reservoirs

Lin, X., & Cheng, Y. F. (2025) · Mechanisms and machine science · doi:10.1007/978-3-031-82907-9_61

Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models

Ogundero, M. A., Adelakin, T., Orolu, K. O., Johnson, I. F., Fashanu, T. A., & Abhulimen, K. E. (2025) · ABUAD Journal of Engineering Research and Development (AJERD) · doi:10.53982/ajerd.2025.0801.30-j

Using ML techniques to predict extubation outcomes for patients with central nervous system injuries in the Yun-Gui Plateau

Chen, Z., Wu, H., Yao, Z., Liu, Q., Zhang, H., Li, X., Yao, L., & Yang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-98861-9

Analyzing Communication and Migration Perceptions Using Machine Learning: A Feature-Based Approach

Tirado-Espín, A., Marcillo-Vera, A., Cáceres-Benítez, K., Almeida-Galárraga, D. A., Garzón, N. O., Moreno-Guaicha, J. A., & Mora, H. C. (2025) · Journalism and Media · doi:10.3390/journalmedia6030112

Construction of Basketball Teaching Evaluation Model Based on Deep Convolutional Neural Network

Wang, B., & Chen, W. (2025) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.387002

CCDC138 overexpression predicts poor prognosis and highlights ciliopathy-linked mechanisms in uterine corpus endometrial carcinoma

Wang, A., Yang, F., Zhang, C., Shi, L., & Fu, H. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1622496

Screening and validation of diagnostic markers for keloids via bioinformatics analysis

Wang, Z., Hu, B., Li, W., Ma, T., & Li, L. (2025) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2025.102219

Bone marrow immune remodeling in depression: TNF/NF-κB mediated leukocyte redistribution and construction of an interpretable predictive model

Zhu, W., Shi, R., Li, X., Cong, J., An, Y., He, M., Zhang, G., Wang, Y., Li, S., Shi, W., et al. (2025) · International Immunopharmacology · doi:10.1016/j.intimp.2025.115487

Predicting Consumer Preferences for Pera and Pulen Rice Varieties Using Machine Learning

Rahmadhani, R. D., Novianty, A., & Pratondo, A. (2025) · IEEE International Symposium on Future Telecommunication Technologies (SOFTT) · doi:10.1109/softt67007.2025.11213295

Noninvasive prediction of Glypican-3 expression in hepatocellular carcinoma using Habitat-based and peritumoral CT radiomics: a nomogram approach

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Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis

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Development of a web platform for predicting fall risk in cardiovascular patients using machine learning

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Assessing plague risk across seven decades of environmental change in Inner Mongolia, China: An interpretable machine learning

Wang, X., Shang, M., Zhao, C., Sun, Y., Feng, L., Xia, L., & Liu, Q. (2026) · iScience · doi:10.1016/j.isci.2026.115611

Landslide susceptibility patterns and their implications for erosional amphitheater formation on Bioko Island (Equatorial Guinea)

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Machine learning combined with multi-omics analysis: identifying nucleotide metabolism-associated immune genes and validating their functions in cardiomyopathy

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Study on Early Pregnancy Diagnosis of Sows Based on Body Fluid Metabolite Detection Combined with Machine Learning Models

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Integrated evaluation and screening of salt-tolerant wheat germplasm and indices

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

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