The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
IJACSA research on disease prediction and diagnosis using machine learning and AI — covering heart, liver, kidney, respiratory, skin, and infectious disease prediction models.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed August 2026
Protecting patient data confidentiality while enabling collaborative machine learning across distributed healthcare institutions remains a major challenge. This study presents ZK-FedMed, a privacy-preserving federated le…
This work proposes a computational framework for modeling semantic relationships between medical specialties using large language models. Forty-four medical specialties officially recognized in France were analyzed using…
Early detection of skin pathology is critical for patient survival and treatment effectiveness, particularly in the case of aggressive malignancies such as melanoma. In this study, we propose a lightweight and explainabl…
Phenotyping inflammatory bowel disease (IBD) from gut microbiome profiles remains challenging due to 93% genus-level zero-inflation, skewed amplicon count distributions, and the practical cost of assembling labelled coho…
The protection of medical images in healthcare services has become important due to the adoption of telemedicine and cloud-based healthcare services. Conventional watermarking methods embed information directly into the…
Accurate classification of heart sounds is critical for the early detection and diagnosis of cardiovascular diseases. This research presents an automated technique for classifying heart sounds into normal, murmur, and ex…
Upon arrival at a hospital, patients require an initial assessment to determine the urgency of their condition and the appropriate medical specialty for their needs. This manual triage process, however, is often time-con…
The rapid integration of artificial intelligence (AI) into healthcare systems has intensified the need for governance frameworks that ensure safety, accountability, ethical, and sustainable deployment. However, existing…
Ensuring strong security readiness in telemedicine is essential to protect patients, safeguard their data, and build confidence and satisfaction in today’s digital healthcare platforms. Hence, this research explores and…
To address the challenges of feature extraction in complex field environments, the limited sensitivity of YOLOv9 to subtle disease features, and the lack of adaptive hyperparameter optimization, this paper proposes an im…
