An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods

This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.