Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
Introduction: Why Data Quality Is Harder Than Ever Data quality has always been important, but in today’s world of ...
Abstract: The manual diagnosis of diabetic retinopathy (DR) is often invasive, time-consuming, expensive, and prone to human error. Additionally, it can be subjective ...
(NASDAQ: NXXT ), a pioneer in AI-driven energy innovation transforming how energy is produced, managed, and delivered, today ...
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted ...
Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
Learn With Jay on MSNOpinion
Supervised learning made easy: Real-world example explained
In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its ...
Abstract: This study aims to compare and improve the performance of four different machine learning algorithms (Naive Bayes, Multi-Layer Perceptron (MLP), Decision Trees, and Support Vector Machines ...
Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
A hybrid model combining LM, GA, and BP neural networks improves TCM's diagnostic accuracy for IPF, achieving 81.22% ...
Objective: Osteoporosis poses a major global public health challenge. The limitations of current diagnostic methods, primarily diagnostic delays in bone density testing, are compounded by the ...
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