Predicting \u27Attention Deficit Hyperactive Disorder\u27 using large scale child data set

Attention deficit hyperactivity disorder (ADHD) is a disorder found in children affecting about 9.5% of American children aged 13 years or more. Every year, the number of children diagnosed with ADHD is increasing. There is no single test that can diagnose ADHD. In fact, a health practitioner has to analyze the behavior of the child to determine if the child has ADHD. He has to gather information about the child, and his/her behavior and environment. Because of all these problems in diagnosis, I propose to use Machine Learning techniques to predict ADHD by using large scale child data set. Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of disease. Lot of new approaches have immerged which allows to develop understanding and provides opportunity to do advanced analysis. Use of classification model in detection has made significant impacts in the detection and diagnosis of diseases. I propose to use binary classification techniques for detection and diagnosis of ADHD