Intelligent Diagnosis of Alzheimer's disease

Abstract

In this paper, we combine the knowledge of decision tree model and K-means clustering algorithm with knowledge of statistics to solve the problems of Diagnosis of Alzheimer's disease using brain structure. In the step of selecting features, this paper considers both data-based and theoretical perspectives for the analysis of variance in Alzheimer's disease. Based on the data perspective, the value of variance represents the magnitude of data volatility, i.e., data with data volatility has more abilities to cause data changes in feature classification. Therefore, in this paper, a total of 28 indicators of Site-faq were selected as necessary indicators and histograms were made to observe the volatility. After the above data processing, we Use the attached structural brain features and cognitive behavioral features to design an intelligent diagnosis of Alzheimer's disease. For multi-classification models, this paper uses the K-means algorithm to cluster and continue to cluster MCI into SMC, EMCI, LMCI to observe the difference between cluster centers. After clustering 53 indicators, this paper analyzes the three types of cluster centers, mainly to analyze the different points in the number of clusters, and analyzes the difference in the mean value of the characteristics of the cluster centers, and at the same time, this paper observes the differences between the characteristics by visualizing the data. In the end, we analyze them in relation to the time points to uncover patterns in the evolution of different categories of diseases over time. At the same time, due to the large amount of data, this paper only selects a small sample of five features for analysis.