Abstract
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.
Highlights
To signify the importance of the proposed scheme, we introduced a widely used feature selection scheme named as Minimum Redundancy - Maximum Relevance. mRMR selects features that are highly correlated with the class and having a low correlation between themselves
Seven independent classifiers are established through the combination of feature extraction methods i.e., Principle Component Analysis (PCA), Kernel PCA (KPCA) features, and mRMR with base classifier support vector machines (SVMs) and Artificial Neural Network (ANN)
The reason for the low performance of mRMR with SVM might be because of insufficient numbers of features selected for the classification task, or the performance might increase if further ranked features are added during the experimentation process
Summary
It is valuable to use facial features for the recognition of patients with endocrine diseases at an early stage This technique is expected to be used for the detection of endocrine diseases and genetic syndromes to shorten the disease diagnosis time and to assist in the staging of endocrine diseases [?]. This research describes the use of an ensemble learning framework to improve the diagnosis of Turner syndrome through facial features recognition. Two widely used ensemble methods, stacking and majority voting, are used to combine multiple basic classifiers with improving the diagnostic of the Turner syndrome.
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