Title: Building an Interpretable Classifier for Brain Tumour Prediction
Speaker: Colleen Charlton
Abstract: Patients with a brain tumour may present with general non-specific symptoms that vary depending on the location and size of the tumour. Thus diagnosis of a brain tumour is difficult, which is compounded by the rarity of the condition.  My project aims to build an interpretable rule-based machine learning model using a UK dataset that can assess a patient’s likelihood of a brain tumour based on their presenting symptoms.  The final interpretable model may act as a second opinion to assist general practitioners in patient referral and diagnosis.