Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...
Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Using longitudinal data on more than 370 000 older Japanese adults, we found that living in constituencies represented by pro-tobacco legislators was associated with higher smoking prevalence. The ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
The aim of this study is to propose a multi-model approach based on random forest regression for Olympic medal result prediction. First, the gold medal and total medal prediction models are ...
ABSTRACT: Predicting knowledge of tuberculosis (TB) could imply several significant changes in the management, control and prevention of this disease. These would be based on advanced technological ...
Despite the substantial increase in egg production and consumption in Türkiye in recent years, price fluctuations remain prevalent. Forecasting consumer prices for eggs is, therefore, a complex ...
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