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.
By Md Shahriar Sajid
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: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.
By Esra Zihni, Katryna Cisek, Hamzah Ziadeh, Hendrik Knoche, Robert Mikulik, John D. Kelleher
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...
By Vincent Roca, Marc Tommasi, Paul Andrey, Aur\'elien Bellet, Markus D. Schirmer, Hilde Henon, Laurent Puy, Julien Ramon, Gr\'egory Kuchcinski, Martin Bretzner, Renaud Lopes
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.
By Lisa Herzog, Jonas Br\"andli, Maurice Schneeberger, Loran Avci, Nordin Dari, Martin H\"ansel, Hakim Baazaoui, Pascal B\"uhler, Susanne Wegener, Beate Sick
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.
By Sagnik Ghosh
The paper presents a hybrid predictive ensemble that merges machine learning and deep neural network techniques to detect and prognosticate cardiovascular disease early. It processes real‑time physiological data from IoMT devices, applying preprocessing, feature selection, and optimized classifiers (SVM, Random Forest, XGBoost) within an ensemble architecture. The cloud‑based system achieves higher accuracy, fewer false positives, and consistent performance on real‑world datasets, supporting continuous patient monitoring and clinical decision support.
By Balaji Venkateswaran
arXiv:2607. 16253v1 Announce Type: cross Abstract: Machine learning-based Type 2 diabetes risk prediction models obtain good internal validation results but lose effectiveness in real-world applications due to deficient external testing and fairness assessment.
By Rajveer Singh Pall, Sameer Yadav, Siddharth Bhalerao, Sourabh Sahu, Ritu Ahluwalia, Bhaskar Awadhiya
arXiv:2608.29419v1 Announce Type: cross
Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over s...
By Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton
arXiv:2606. 09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks.
By Xinze Zhang
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
By Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos
arXiv:2606. 00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment.
By Jeba Maliha, Md Rafiul Kabir