Fairness-aware ICU mortality prediction using MIMIC-III data and Group-Aware SMOTE
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Updated
Jul 18, 2025 - HTML
Fairness-aware ICU mortality prediction using MIMIC-III data and Group-Aware SMOTE
This project uses four algorithms (Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors) in predicting diabetes risk and susceptibility. The outcomes showed that the Logistic Regression algorithm had the highest accuracy in predicting diabetes using the diabetes dataset obtained from Kaggle.
CT-MatchBERT: A Transformer-Based Trial Matching Framework
AI-powered disease prediction model using SVM, Naive Bayes, and Random Forest trained on 132-symptom dataset
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