In our study, detection and estimation of Down Syndrome disease is maintained by analyzing the serum marker levels measured from the blood samples of the patients as well as the maternal age. We dealt with a real medical data set provided by Department of Obstetrics and Gynecology of University of Debrecen. Here, we used supervised learning methods including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Naive-Bayesian Classification and Artificial Neural Network (ANN) on a software called RapidMiner Studio[ 3] that is an environment for designing advanced analytic processes with machine learning, data mining, text mining, predictive analytic and business analytic. Along with RapidMiner Studio, we used IBM SPSS and Rstudio softwares to analyze and visualize our medical data set.