Thi-Thu-Hien Pham

Work place: School of Biomedical Engineering, International University, Vietnam National University HCM City, HCM City, Vietnam



Research Interests:


Pham Thi Thu Hien received the B.S. degree in mechatronics from the Ho Chi Minh City University of Technology-Vietnam National University, Ho Chi Minh City, Vietnam, in 2003 and the M.S. and Ph.D. degrees in mechanical engineering from Southern Taiwan University of Technology and National Cheng Kung University, Tainan, Taiwan, in 2007 and 2012, respectively. She is currently a Head of Biomedical Photonics Lab in School of Biomedical Engineering, International University-Vietnam National University HCMC, Ho Chi Minh City, Vietnam. Her research interests are in the areas of polarized light-tissue studies, polarimetry, optical techniques in precision measurement to determine the optical properties of bio-samples (glucose, collagen, and tumor) or applied Artificial Intelligence (AI) models for cancer detection (skin, liver, blood, and breast), noninvasive glucose measurement, cell/tissue characterization, and laser/LED applications in treatment.

Author Articles
AI-powered Predictive Model for Stroke and Diabetes Diagnostic

By Ngoc-Bich Le Thi-Thu-Hien Pham Sy-Hoang Nguyen Nhat-Minh Nguyen Tan-Nhu Nguyen

DOI:, Pub. Date: 8 Feb. 2024

Research efforts in the prediction of stroke and diabetes prioritize early detection in order to enhance patient outcomes. To achieve this, a variety of methodologies are integrated. Existing studies, on the other hand, are marred by imbalanced datasets, lack of diversity in their datasets, potential bias, and inadequate model comparisons; these flaws underscore the necessity for more comprehensive and inclusive research methodologies. This paper provides a thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes. The research employed widely used algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost Classifier, to examine medical data and derive significant findings. The XGBoost Classifier demonstrated superior performance, with an outstanding accuracy, precision, recall, and F1-score of 87.5%. The comparative examination of the algorithms indicated that the Decision Tree, Random Forest, and XGBoost classifiers consistently exhibited strong performance across all measures. The models demonstrated impressive discrimination capabilities, with the XGBoost Classifier and Random Forest reaching accuracy rates of roughly 87.5% and 86.5% respectively. The Decision Tree Classifier exhibited notable performance, with an accuracy rate of 83%. The overall accuracy of the models was evident in the F1-score, a metric that incorporates recall and precision, where the XGBoost model exhibited a marginal improvement of 2% over the Random Forest and Decision Tree models, and 4.25 percent over the last two. The aforementioned results underscore the effectiveness of the XGBoost Classifier, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes. Furthermore, combining datasets improves model performance by utilizing relative features. This integrated dataset improves the model's efficiency and creates a resilient and comprehensive prediction model, improving healthcare outcomes. The findings of this research make a valuable contribution to the advancement of AI-driven diagnostic systems, hence enhancing the quality of healthcare decision-making.

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