Bayesian Stroke Prediction (CS3263 - Foundation of AI)

• Year: 2023
• Team Lead: Led a team of three students
• Award: Best Project Award in CS3263 under Professor Leong Tze Yun

This project involved leading a team of three to develop a sophisticated Bayesian modeling approach for predicting stroke risk. The model was meticulously designed to evaluate and quantify the likelihood of stroke occurrence by analyzing a comprehensive set of medical and demographic factors, such as age, blood pressure, cholesterol levels, and family history. By leveraging Bayesian inference, the model was able to incorporate prior knowledge and update predictions as new data became available, resulting in highly accurate and personalized risk assessments. The insights provided by this model are invaluable for both patients and healthcare professionals in making informed decisions about preventive care and early interventions.

The project’s innovative approach and its potential impact on healthcare were recognized with the Best Project Award in the CS3263 course at NUS, under the guidance and supervision of Professor Leong Tze Yun. This accolade underscores the project’s excellence in both technical execution and practical relevance.