To derive important insights from biomedical data using machine learning models.
This is a collaborative work between:
The field of medicine has witnessed great improvement because of technological advancements. It has become easier to collect a range of healthcare data due to low-cost wearable devices. The insights acquired from mining clinical data have proved useful for decision making in improving healthcare and reducing healthcare costs.
In this research theme, we perform proactive medical diagnostics using statistical and machine-learning techniques. Using a publicly available dataset of electronic health records, we identify the key risk factors associated with stroke.
We show the importance of each patient attribute in predicting the occurrence of stroke using a Learning Vector Quantization (LVQ) model.
Please refer to the publications.