Abstract: Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis ...

Abstract: Fake news is spreading more widely as a result of the exponential rise of digital news content, which is a major danger to public trust and democratic societies. This project offers a ...

The Titanic dataset is one of the most widely used datasets in introductory machine learning and data science. It contains demographic and passenger information such as age, sex, ticket class, fare, ...

Researchers developed a new model to predict the likelihood of critical illness in patients with connective-tissue disease-associated ILD.

Glucose-to-potassium ratio shows a J-shaped link with AKI after traumatic brain injury, with high levels predicting increased ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

Objectives: New effective therapies with particularly good effect on joint destruction have highlighted the need for reliable predictors of radiographic progression in rheumatoid arthritis (RA). Our ...

Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...

Introduction People diagnosed with tuberculosis (TB) after death (postmortem) experience the ultimate diagnostic delay. We ...

BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the ...

Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor ...