Accurate prediction of agricultural production costs is crucial for sustainable development in Egypt, where productivity is highly sensitive to fluctuating economic and environmental conditions. This study introduces a hybrid machine learning model t...
Random Forest (RF) is a powerful ensemble-based supervised machine learning technique that builds multiple decision trees using bootstrap aggregating and random feature selection to improve classification and regression accuracy while reducing overfi...
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Jun 26, 2025
OBJECTIVES: Population-wide screening for primary open-angle glaucoma (glaucoma) is typically not cost-effective because of low prevalence and high costs. We evaluated the cost-effectiveness of repeated artificial intelligence (AI)-based glaucoma scr...
Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a pressing need for early diagnostic methods for HCV. This study investigates the prognostic accuracy o...
Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas
Jun 16, 2025
Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a decision tree. We tested if the suggested components of the actual classification (metabolic dysfun...
Anais da Academia Brasileira de Ciencias
Jun 16, 2025
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...
Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy, neuropathy, foot problems, heart attacks, and strokes if left unchecked. Prompt detection and diagn...
BACKGROUND: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to hea...
Due to imbalanced data values and high-dimensional features of lung cancer from CT scans images creates significant challenges in clinical research. The improper classification of these images leads towards higher complexity in classification process...
MultiOmicsAgent (MOAgent) is an innovative, Python-based open-source tool for biomarker discovery, utilizing machine learning techniques, specifically extreme gradient-boosted decision trees, to process multiomics data. With its cross-platform compat...
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