Combining Random Forest, Neural Networks, And Association Rules For Student Grade Prediction And Course Recommendations
VOLUME 22, 2025
The Role of Targeted Infra-popliteal Endovascular Angioplasty to Treat Diabetic Foot Ulcers Using the Angiosome Model: A Systematic Review
VOLUME 6, 2023
Sami Alghmadi
Abstract
This study proposes an ensemble-based approach for predicting university student performance and recommending optimal course combinations. The approach integrates Random Forest (RF), Long Short-Term Memory networks (LSTM) with Attention mechanisms, and Association Rule Mining (ARM) to address both individual academic forecasting and institutional course planning. RF is employed for effective feature selection and classification, while LSTM-A captures temporal patterns in students' academic trajectories. ARM is used to extract interpretable associations between course groupings and performance trends. The dataset contains detailed transcript and study plan records from 107 undergraduate students across 17 semesters. The experimental evaluation shows that the ensemble model achieves an accuracy of 82% and a macro-F1 score of 80%, outperforming traditional machine learning techniques. Additionally, the framework successfully identifies at-risk students with 85% accuracy in early semesters, supporting its potential use for academic advising and early intervention strategies.
Keywords :
Educational Data Mining, Ensemble Learning, Student Performance Prediction, Association Rule Mining, Course Recommendation Systems, Random Forest, Long Short-Term Memory Networks, Early Intervention.
Erin Saricilar
Lecture in accounting. University of Basrah, College of Administration and Economics, Department of Accounting.
Lecture in accounting. University of Basrah, College of Administration and Economics, Department of Accounting.
Abstract
Atherosclerotic disease significantly impacts patients with type 2 diabetes, who often present with recalcitrant peripheral ulcers. The angiosome model of the foot presents an opportunity to perform direct angiosome-targeted endovascular interventions to maximise both wound healing and limb salvage. A systematic review was performed, with 17 studies included in the final review. Below-the-knee endovascular interventions present significant technical challenges, with technical success depending on the length of lesion being treated and the number of angiosomes that require treatment. Wound healing was significantly improved with direct angiosome-targeted angioplasty, as was limb salvage, with a significant increase in survival without major amputation. Indirect angioplasty, where the intervention is applied to collateral vessels to the angiosomes, yielded similar results to direct angiosome-targeted angioplasty. Applying the angiosome model of the foot in direct angiosome-targeted angioplasty improves outcomes for patients with recalcitrant diabetic foot ulcers in terms of primary wound healing, mean time for complete wound healing and major amputation-free survival.
Keywords :
Diabetic foot ulcer, angiosome, angioplasty