Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

Read more about AI can’t deliver climate gains without strong governance and capacity building on Devdiscourse ...

The results show that the Decision Tree model emerged as the top-performing algorithm, achieving an accuracy rate of 99.36 percent. Random Forest followed closely with 99.27 percent accuracy, while ...

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...

Machine learning may help predict Fragile X-associated tremor syndrome earlier, enabling planning, monitoring, and timely ...

HFpEF in hypertrophic cardiomyopathy predicts adverse outcomes. Discover how machine learning improves risk assessment.

Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.

Scientists at the European Centre for Medium-Range Weather Forecasts have unveiled a machine learning technique that pinpoints optimal locations for tree planting, offering a powerful tool for climate ...

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential roughly 28–34 times greater over a 100-year timescale. Major sources include ...

As atmospheric carbon dioxide levels continue to rise, accurately measuring the carbon stored in the world's forests has become more critical than ever. Forests are vital carbon sinks, but traditional ...