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 ...

Read more about From disease detection to biomass forecasting: AI improves aquaculture risk strategy on Devdiscourse ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...

Five principal factors were identified as significantly associated with DPN risk: age (OR = 1.257, 95% CI [1.188–1.367], p < 0.001), serum 25(OH)D3 levels (OR = 0.791, 95% CI [0.759–0.854], p < 0.001) ...

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 ...

Random forest machine learning regression and interactive web application using home loan data to help determine algorithmic discrimination toward race and gender in ...

Abstract: Load forecasting, a crucial aspect of energy management, involves predicting the future electricity demand based on historical data. In the context of monthly time series analysis, this ...