Bruce Nguyen

Bruce Nguyen, Yan Zhang

Diabetes is a prevalent chronic disease with significant health and economic burdens worldwide. Early prediction and diagnosis can aid in effective management and prevention of complications. This study explores the use of machine learning models to predict diabetes based on lifestyle factors using data from the Behavioral Risk Factor Surveillance System (BRFSS) 2015 survey. The dataset consists of 21 lifestyle and health-related features, capturing aspects such as physical activity, diet, mental health, and socioeconomic status. Three classification models, Decision Tree, K-Nearest Neighbors (KNN), and Logistic Regression, are implemented and evaluated to determine their predictive performance. The models are trained and tested using a balanced dataset, and their performances are assessed based on accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree, KNN, and Logistic Regression achieve an accuracy of 0.74, 0.72, and 0.75, respectively, with varying strengths in precision and recall. The findings highlight the potential of machine learning in diabetes prediction and suggest future improvements through feature selection and ensemble learning techniques.

Bruce Nguyen, Shaoxiong Ji

The massive scale and growth of textual biomedical data have made its indexing and classification increasingly important. However, existing research on this topic mainly utilized convolutional and recurrent neural networks, which generally achieve inferior performance than the novel transformers. On the other hand, systems that apply transformers only focus on the target documents, overlooking the rich semantic information that label descriptions contain. To address this gap, we develop a transformer-based biomedical text classifier that considers label information. The system achieves this with a label attention module incorporated into the fine-tuning process of pretrained language models (PTMs). Our results on two public medical datasets show that the proposed fine-tuning scheme outperforms the vanilla PTMs and state-of-the-art models.