F012 A machine learning model for improving the AAO estimation for HD

<h3></h3> The Enroll-HD study is an integrated clinical research platform operating in the field of neurology, and is a large effort by the HD community to address the research and clinical needs of HD. It encompasses more than 25,000 participants, including controls, manifest HD and pre-manifest HD. The size of Enroll-HD makes it amenable to machine learning (ML) algorithms for discovering patterns and making predictions that pertain to several aspects of the disease, such as the prediction of the age of onset (AAO). The AAO is one of the most important variables in the Enroll-HD, and it describes the point in time that the first symptoms of HD occur. In this project, we developed a prediction model for improving the estimation of the AAO. We carefully selected the study population, and we included participants that have entered the Enroll-HD as pre-manifest and progressed to manifest. We performed feature selection to select the most critical variables for our prediction models. Our ML model achieved a performance of around 70% with an average error of 5 years. The model was composed by variables such as the CAG repeat, parent age of onset, marital status and years of smoking. Our results demonstrate that we are able to improve the estimation of the AAO for HD gene carriers. This is very important for selecting populations for clinical trials that are focusing on neuroprotective treatments. Timely treatments can result in a better quality of life not only for HD patients but also for their caregivers.