Localization And Classification Of Attention Deficit Hyperactivity Disorder Based On Random Forest

With the development of imaging and other auxiliary diagnostic technologies,more and more interdisciplinary researchers have been devoted to exploring the internal operation of human brain and its relationship with neurology and neurodegenerative diseases based on different data.However,the classical statistical method is not effective in the treatment of high dimensional medical images.The main goal of this study is based on high-dimensional image data of attention deficit hyperactivity disorder(Attention Deficit Hyperactivity Disorder,ADHD),also known as childhood hyperactivity disorder(ADHD),is one of the most common childhood diseases that can continue into adolescence and adulthood.Symptoms include difficulty in concentration,difficulty in controlling behavior and hyperactivity.In recent years,in the study of the image data of the disease,the traditional statistical methods directly quantize the imaging data to get ultra-high dimension,which will cause serious damage to the imaging data structure,ignoring the structural dependence of the data,resulting in the loss of a lot of important structural information,so the processing of this kind of data poses an unprecedented challenge to the classical statistical methods.The experimental data ADHD-200 used in this paper is the nuclear magnetic resonance image(MRI)data of attention deficit hyperactivity disorder.Based on this data,a data preprocessing method of multi-dimensional segmentation is proposed,which is combined with random forest classification to classify the segmented data.Then the tasks of classification and target detection are then finished according to the proposed multi-dimensional integration algorithm,which mainly completes the improvement of diagnosis accuracy,lesion region detection and threshold selection of three aspects of research.The average correct diagnosis rate of data ADHD-200 by researchers around the world is about 60%,and this method can reach 75.4% eventually.In addition,experiments are carried out in both simulated data and real data and the signal region is detected.Finally,combined with the background of practical problems,this paper gives the selection method of threshold to help medical researchers to select the threshold which is beneficial to the research.