The paper presents an intelligent system that predicts stroke risk using eleven clinical features and evaluates seven supervised machine learning algorithms. Ensemble methods—Random Forest, Stacking Classifier, and Bagging Classifier—achieved the highest accuracies, reaching 99.52%, while other models such as KNN, TabNet, and a custom feedforward network also performed well. The study demonstrates that ensemble approaches are particularly effective for stroke classification tasks.
Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early diagnosis and preventive measures can greatly reduce...
arXiv:2501. 00048v2 Announce Type: replace-cross Abstract: Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes.
arXiv:2506.15626v3 Announce Type: replace-cross Abstract: $\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust...
arXiv:2607. 02142v1 Announce Type: cross Abstract: Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities.
arXiv:2608. 05203v1 Announce Type: new Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians' reasoning.
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.
arXiv:2606. 29106v1 Announce Type: cross Abstract: Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential.
The study evaluates several machine‑learning classifiers for predicting heart disease using two public datasets, each with 14 health‑related attributes. Performance metrics such as MAE, RAE, accuracy, precision, recall, and F‑measure were used, revealing that SVM performed best on the UCI data while Simple Cart excelled on the Kaggle data. The authors conclude that well‑tuned ML models can aid early heart‑disease diagnosis and suggest future work on hybrid methods and newer datasets.
Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential. While many deep CNNs have been developed for MRI-based classification of neurological disorders, most are optimized for binary tasks and often fail to capture the multi-class features needed to distinguish subtle anatomical differences across conditions.
arXiv:2504. 06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited.
The study evaluated a pragmatic deep‑learning approach for segmenting acute ischemic stroke lesions on diffusion‑weighted MRI. Using a self‑configured nnU‑Net trained on 1,744 cases and tested on 436, the baseline model achieved a median Dice similarity coefficient of 0.84, outperforming the DeepISLES ensemble, especially for smaller infarcts. The approach required minimal preprocessing and fast inference, suggesting it could streamline clinical stroke imaging workflows.
arXiv:2606. 26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver.