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 ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...
ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...
Orthopedic surgery is becoming a data-dense discipline (1). Clinical records, perioperative physiology, radiological imaging, and patient-reported outcomes now coexist in routine care, yet they are ...
Objective Missed hospital appointments are common among outpatients and have significant clinical and economic consequences. The purpose of this study is to develop a predictive model of missed ...
The chief complaints, present illness, past medical history and vital signs of the patients from the internal medicine departments of the First Affiliated Hospital and the Second Affiliated Hospital ...
ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results