Nurmisba Usman

Nurmisba Usman is an informatics graduate of Universitas Muhammadiyah Makassar. Her research applies artificial intelligence to healthcare, including optimizing k-means clustering with the elbow method to predict hospital blood requirements, grid search hyperparameter tuning of decision trees for diabetes prediction, Swin Transformer enhanced with out-of-distribution detection for robust ischemic stroke diagnosis from CT images, the Edmonton Symptom Assessment System (ESAS) in patients with diabetic foot ulcers, and knowledge-validated conversational systems with a hybrid BERT-RAG model.

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You can find more information on her LinkedIn and her Google Scholar.