Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
North Penn Now

Machine Learning Using Python: A Complete Learning Path With Practical Projects

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
IEEE

Fuzzy LASSO Logistic Regression: A Regularized Framework for Interpretable Binary Classification Under Uncertainty

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Hosted on MSN

Build logistic regression in Python from scratch easily

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 ...
Scientific Research Publishing

Johnson, A. and Lee, M. (2022) Predictive Modeling for Cardiovascular Risk Assessment Using Logistic Regression and Deep Learning. Journal of Biomedical Informatics, 130 ...

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 ...
Frontiers

Predictive regression models for cognitive impairment, dementia, and Alzheimer’s disease using real-world electronic health records

The aim of this non-interventional, case–control pilot study was to identify factors associated with cognitive impairment, dementia, and Alzheimer’s disease (AD) using a real-world dataset from ...
Frontiers

Editorial: Harnessing artificial intelligence for multimodal predictive modeling in orthopedic surgery

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 ...
BMJ

Development and evaluation of prediction models to improve the hospital appointments overbooking strategy at a large tertiary care hospital in the Sultanate of Oman: a ...

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 ...