The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
The Lancet

Machine learning prediction of 1-year mortality in older patients with heart failure: a nationwide, multicenter, prospective cohort study

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
GitHub

phd-preparation

End-to-end explainable AI pipeline for medical classification using Random Forest and XGBoost with SHAP and LIME for global and local interpretability. Designed for transparent, trustworthy machine ...
Frontiers

Accurate prediction of sepsis from pediatric emergency department to PICU using a machine-learning model

Background: Timely identification of pediatric sepsis remains a critical challenge in emergency and intensive care settings due to the heterogeneous clinical presentations across age groups. Existing ...
Sports Illustrated

GHSA Proposes Major Reclassification Changes to Level the Playing Field in Georgia High School Sports

Georgia officials are drafting new rules that would allow struggling programs to move down in classification and reshape playoff qualifications, with key votes coming in October The Georgia High ...
IEEE

Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm

Abstract: Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through ...
Scientific Research Publishing

Sahin, E.K. (2020) Assessing the Predictive Capability of Ensemble Tree Methods for Landslide Susceptibility Mapping Using XGBoost, Gradient Boosting Machine, and Random Forest ...

ABSTRACT: Neuroleptic Malignant Syndrome (NMS) is a rare but life-threatening neurological emergency that arises primarily from the use of dopamine antagonist antipsychotic medications. Clinically, it ...
Scientific Research Publishing

Cygu, S., Seow, H., Dushoff, J. and Bolker, B.M. (2023) Comparing Machine Learning Approaches to Incorporate Time-Varying Covariates in Predicting Cancer Survival Time ...

ABSTRACT: Neuroleptic Malignant Syndrome (NMS) is a rare but life-threatening neurological emergency that arises primarily from the use of dopamine antagonist antipsychotic medications. Clinically, it ...
GitHub

Consumer Complaints Text Classification Using Machine Learning

This project aims to build a multi-class text classification model for consumer complaint narratives.It categorizes complaints into four classes: Credit Reporting, Debt Collection, Consumer Loan, and ...