Tag: SHAP interpretability

Researchers have developed DeepOSSA, a deep learning architecture that integrates Transformer attention with survival analysis to improve individualized prognostic predictions ...

Researchers have developed an intelligent evaluation model pairing an autoencoder and a chaotic dragonfly algorithm with a feed-forward neural network ...

In a single-center retrospective study of 240 pyogenic spondylitis patients, a random forest model using six routine early indicators accurately ...

Researchers developed LogISEL, an interpretable stacked machine learning framework that forecasts slope movement classes ten minutes ahead across diverse Himalayan ...

Researchers developed a sequence-based machine learning model combining dinucleotide autocorrelation features and protein language model embeddings that achieved an area ...

Researchers have developed DynaMELD, a dynamic deep learning survival model that tracks longitudinal biomarker trajectories to improve mortality prediction and ...

Researchers developed a pruned deep feedforward neural network that maintains strong metastatic breast cancer classification performance while reducing clinical inputs ...

An integrated machine-learning framework using XGBoost achieved 99.94 percent accuracy in classifying sorghum yields and modeled potential conflict-related crop losses ...

A 21-year high-resolution satellite and machine learning analysis reveals an expanding air pollution disparity between northern and southern Ghana.

A simplified 11-variable random forest model predicted in-hospital mortality among ICU patients surviving 24 hours after traumatic brain injury with ...