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
With the recent impact of viral infections and pandemics – akin to a recent global healthcare emergency due to COVID-19 - there is an urgent need for mass-scale testing and vaccination initiatives for tackling the health…
Heart disease remains a global health concern, demanding early and accurate prediction for improved patient outcomes. Machine learning offers promising tools, but existing methods face accuracy, class imbalance, and over…
Glaucoma and cataracts are leading causes of blindness worldwide, resulting in significant vision loss and quality of life impairment. Early detection and diagnosis are crucial for effective treatment and prevention of f…
Data classification, a crucial practice in information management, involves categorizing data based on its sensitivity to determine appropriate access levels and protection measures. This paper explores the utilization o…
Alzheimer's disease (AD) poses a significant challenge to modern healthcare, as effective treatment remains elusive. Drugs may slow down the progress of the disease, but there is currently no cure for it. Early AD identi…
It is important to note that the cure rates in cases of advanced stages of lung cancer are remarkably low, which stresses out the importance for early detection as means to increase survival chances. A strong area of foc…
In the past decade, Electronic Health Records (EHRs) based on clouds have become popular in empowering remote patient monitoring. The rise of Health 4.0, which includes using system elements and cloud services to access…
Aiming at addressing the challenges of large model parameters, high computational cost, and low accuracy of the traditional tea disease identification model, an improved MobileNet model integrated spatial and channel att…
In modern medical diagnostics, Deep Learning models are commonly used for illness diagnosis, especially over X-ray chest images. Deep Learning approaches provide unmatched promise for early identification, prognosis, and…
In the expanding field of medical imaging, precise segmentation of anatomical structures is critical for accurate diagnosis and therapeutic interventions. This research paper introduces an innovative approach, building u…
