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 ...

Abstract: This research paper presents a comprehensive study on the prediction of energy consumption for household appliances using machine learning algorithms, with a focus on regression models. The ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

Predicting performance for large-scale industrial systems—like Google’s Borg compute clusters—has traditionally required extensive domain-specific feature engineering and tabular data representations, ...

"Global climate models are essential for policy planning, but these models often struggle with 'compound extreme events,' which is when extreme events happen in short succession—such as when extreme ...

Abstract: In this paper, a regression based machine learning model (Lasso) is used for the design of cavity backed slotted antenna. This type of antenna is commonly used in military and aviation ...

web-based house price prediction app using Flask and machine learning. Trained on the Boston Housing dataset with error handling and UI enhancements.

This study analyzed 1,544 diabetic patients from the First Affiliated Hospital of Shandong First Medical University, who were randomly divided into a training cohort (n = 1,082) and a testing cohort ...