Student mental health and academic stress are significant concerns in higher education, creating a need for effective approaches to early risk identification. This study proposes a multiclass predictive framework for academic stress and mental health risk classification among students. The framework was developed using a Kaggle dataset containing 25,000 records with demographic, academic, behavioral, psychological, lifestyle, and medical attributes. A unified comparison was conducted across six conventional machine learning algorithms, namely Decision Tree, Random Forest, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Naïve Bayes, and three deep learning architectures, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM). Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), applied only to the training data, and model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. CatBoost and BiLSTM achieved the highest classification accuracy of 97%, with weighted F1-scores of 0.97 and macro F1-scores of 0.96. The results indicate that ensemble and deep learning approaches can effectively support multiclass mental health risk classification. Because the provenance and label construction of the secondary dataset are not documented, the results should be read as within-dataset performance. The findings provide a basis for future real-world validation and decision-support applications.
Shoaib Ahmad, Ali Imran, Tahoona Kshif et al.· ICCK Journal of Software Eng...· 0 citations
Brain tumor segmentation plays an important role in clinical diagnosis, treatment planning, and neuro-oncology assessment using multimodal Magnetic Resonance Imaging (MRI) data. However, conventional centralized deep learning systems often face limitations associated with patient data privacy, secure inter-institutional collaboration, and limited model interpretability. This study presents a decentralized and privacy-preserving brain tumor segmentation framework that integrates Federated Learning (FL), a blockchain-inspired audit and coordination mechanism, and Explainable Artificial Intelligence (XAI) within a collaborative medical imaging environment. A 3D U-Net architecture was trained on the BraTS 2020 dataset under a simulated multi-institutional federated setting using non-IID MRI data distributions. Federated Averaging (FedAvg) was employed for global model aggregation, while a blockchain-inspired permissioned ledger mechanism was used to record model update hashes and aggregation metadata for auditability across communication rounds. Grad CAM and LIME were incorporated to provide interpretable visualization of tumor related regions contributing to segmentation predictions. Experimental evaluation demonstrated stable convergence behavior with an overall voxel accuracy of 98.52%, a weighted F1 score of 98.3%, and Dice coefficient improvement from 0.752 to 0.830 during federated training. The generated segmentation outputs showed strong agreement with manually annotated tumor regions while preserving decentralized data privacy. The proposed framework contributes a secure, interpretable, and collaborative segmentation pipeline for trustworthy brain tumor analysis across distributed healthcare environments.
Aiza Mukhtar, Aamir Ali, Wajiha Farooq et al.· ICCK Journal of Image Analys...· 0 citations
Findings indicate that explainable machine learning models, particularly KNN and XGBoost, provide accurate and interpretable decision support for early PCOS screening, enabling timely intervention and offering a promising foundation for intelligent healthcare decision-support systems.
Sana Rubab, Musarrat Shaheen, Zohrain Tabassum et al.· Biomedical Informatics and S...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
