Vertigo is a common but under-treated medical condition with a lifetime prevalence up to 40%. Currently, the diagnosis and treatment of vertigo is done primarily by specialists who represent only 1% of the doctors in Australia. My project, in collaboration with clinicians, data scientists and statisticians, will use machine learning techniques to develop a “virtual expert” diagnostic tool to assist the diagnosis of vertigo in the hospital emergency room, general practice, and in outpatient clinics. This will be accomplished by using a structured history, eye examination for abnormal eye-movements (nystagmus), and the results of inner-ear balance tests. Feature Engineering will help in the selection of the most important test results required for accurate diagnosis. We will then build the Artificial Intelligence prototype model where machine learning techniques will help develop an algorithm to analyse, classify, cluster, and predict the diagnosis of vertigo in patients with greater precision.