A genetic disease or disorders is a hereditary issue caused by one or more abnormalities formed in the genome. Genetic disorders can be monogenic, multifactorial, or chromosomal. Like genetic disorders, facial features are also passed down genetically. This paper proposes to identify genetic disorders from facial features. However, it does not explain which facial features led to its prediction. In order to overcome the issues, face reflexology regions are analysed to predict the genetic diseases. Face reflexology regions are related to the internal organs and structure of the body. Genetic faces are analyzed with respect to face reflexology regions for the prediction of genetic diseases. Feature vectors are generated for the reflexology regions using Local binary pattern (LBP) with the combination of high frequency and low frequency textures. The Euclidean distance weight function is used for prediction of diseases using the feature vectors. The proposed method is not only using single face reflexology regions, but combined reflexology regions of n persons are used for finding multiple possibility of diseases. Based on the statistical measure analysis, the proposed algorithm works well in extracting the features for identifying the diseases linked to genetic disorders, potentially speeding up diagnosis of diseases.
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