TechAnnouncer

Demystifying Machine Languages: Practical Examples and Core Concepts

Machine learning can sound pretty complicated, right? Like something only super-smart tech people get. But honestly, it’s ...
AZoRobotics

Machine Learning Models Predict Stroke Risk in Atrial Fibrillation

Innovative machine learning models using routine clinical data offer superior stroke risk prediction in atrial fibrillation, ...
Open Heart

Prognostic value of cardiac fibroblast activation heterogeneity assessed by FAPI PET radiomics in hypertrophic cardiomyopathy: a proof-of-concept study

Background Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death (SCD), and myocardial fibrosis plays a central role in the pathophysiology process.
American College of Cardiology

Machine Learning Outperforms Traditional Methods to Predict Adverse Events in LAAO Patients

EXtreme Gradient Boosting (XGBoost), a machine learning model, outperformed more traditional methods for predicting composite major adverse events (MAEs) and many individual events in patients ...
GitHub

[OTHER]：Add Federated Learning (FL) - Horizontal Federated Logistic Regression Implementation

I would like to contribute a lightweight and optimized implementation of Horizontal Federated Logistic Regression (2025 optimized version) to this project. This implementation is tailored for ...
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 ...
Scientific Research Publishing

Joshi, R.D. and Dhakal, C.K. (2021) Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches. International Journal of Environmental Research and ...

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 ...