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Comparative Analysis of Machine Learning Algorithms for Genomic Data

Auti, R., Bhatt, A., & Tidake, S. (2023) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei58380.2023.10406455 · Cited by 1

Blood Glucose Concentration Prediction Based on Double Decomposition and Deep Extreme Learning Machine Optimized by Nonlinear Marine Predator Algorithm

Shen, Y., Li, D., Wang, W., & Xu, D. (2024) · Mathematics · doi:10.3390/math12233708 · Cited by 1

Designing and Evaluating Heterogeneous Ensembles for Blood Glucose Level Forecasting

Benaida, M., Abnane, I., & Idri, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96997-3_8 · Cited by 1

Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study

Zhang, Z., Wu, J., Duan, Y., Liu, L., Liu, Y., Wang, J., Li, X., & Gao, Z. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2537920 · Cited by 1

An improved marine predators algorithm for shipment status time estimation and regression problems

Özdemir, R., Taşyürek, M., & Aslantaş, V. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02967-5 · Cited by 1

A Detection Method for Nodular Shadows from Temporal Subtraction Images Using A Machine Learning Technique Incorporated Radiomics Features

Baba, N., Kamiya, T., Terasawa, T., Aoki, T., & Kido, S. (2023) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas59377.2023.10316767 · Cited by 1

Evaluation of the Prediction Algorithms for the Diagnosis of Hepatic Dysfunction

Arpacı, S. A., & VARLI, S. (2023) · Nevşehir Bilim ve Teknoloji Dergisi · doi:10.17100/nevbiltek.1256873 · Cited by 1

Neonatal Jaundice detection using machine-learning algorithms: A comparative study

Abdulrazzak, A. Y., Mohammed, S. L., Al‐Naji, A., & Chahl, J. (2024) · AIP conference proceedings · doi:10.1063/5.0236954 · Cited by 1

Identifying suspicious internet threat exchanges using machine learning algorithms to ensure privacy and cybersecurity in the USA

Farabi, S. F., Biswas, B., Sarkar, M. I., Mohammad, N., Alam, M. Z., Hasan, R., & Abdullah, M. (2024) · Journal of Infrastructure Policy and Development · doi:10.24294/jipd8848 · Cited by 1

Comparing the influence of social risk factors on machine learning model performance across racial and ethnic groups in home healthcare

Hobensack, M., Davoudi, A., Song, J., Cato, K., Bowles, K. H., & Topaz, M. (2025) · Nursing Outlook · doi:10.1016/j.outlook.2025.102431 · Cited by 1

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food ProteinsPreprint

Wang, L., Niu, D., Zhao, X., Wang, X., Hao, M., & Che, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.10.434710 · Cited by 1

Comparison and Hyperparameter Optimization of Ensemble Learning Models for Soil Moisture Prediction Using Remote Sensing DataPreprint

Liaghat, A., ربانیها, ح., & Kaviani, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5703504 · Cited by 1

RG Hyperparameter Optimization Approach for Improved Indirect Prediction of Blood Glucose Levels by Boosting Ensemble Learning

Wang, Y., Zhang, H., An, Y., Ji, Z., & Ганчев, И. (2021) · Electronics · doi:10.3390/electronics10151797 · Cited by 17

Blood Glucose Prediction Method Based on Particle Swarm Optimization and Model Fusion

Xu, H., Bao, S., Zhang, X., Liu, S., Wei, J., & Ji, Y. (2022) · Diagnostics · doi:10.3390/diagnostics12123062 · Cited by 7

LightGBM Predictive Framework for Blood Glucose Analysis: Leveraging PPG, Age, and BMI for Classification and Regression

Kusriyanto, M., Khairudin, M., & Asnawi, R. (2025) · IEEE International Biomedical Instrumentation and Technology Conference (IBITeC) · doi:10.1109/ibitec66306.2025.11472909

Predicting Blood Glucose Levels with Advanced Time Series Models

Kandemir, T., Knauer, M., Coskunyürek, E., & Enzweiler, M. (2026) · Proceedings in Technology Transfer · doi:10.1007/978-3-032-10569-1_1 · Cited by 1

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

Sampa, M. B., Biswas, T., Rahman, M. S., Aziz, N. H. A., Hossain, N., & Aziz, N. A. A. (2023) · JMIR Diabetes · doi:10.2196/49113 · Cited by 11