Detection and Estimation of Down Syndrome Genes by Machine Learning Techniques

Abstract

Down syndrome is accepted as the common birth defect in population and diagnosed as more physical development with less cognitive activity than an average human. Early diagnosis of disease play important role for the patient future life. Computer aided systems, in terms of artificial intelligence, results more accurate and consistent diagnosis in the detection and estimation of down syndrome genes compare to doctor decisions. In this study, detection and estimation of down syndrome disease is maintained by analyzing the protein levels in genes. In this sense, a Decision Support System based on machine learning techniques are proposed to estimate the down syndrome automatically. Additionally, another technique named as Principal Component Analyses are performed to eliminate multi proteins in genes into fewer number to achieve the same success with less information.