Due to the current situation of mental health illness, which causing a huge impact on the society. In this paper, an attempt has been made to analyses and predict the data from ANDI using single and composite algorithms. This paper used chi-square test, Spearmans correlation coefficient and maximum mutual information number, cost-sensitive learning, SMOTE, ADASYN, SMOTE+ENN, SMOTE+TOMEK to investigations. Specifically, this paper adopted the random forest to fill the data, and besides, given the fact that the data shows the characteristics of imbalance, this paper identifies the method of SMOTE TOMEK integrated sampling, XGBOOST and Bayesian optimization scheme to give the best performance and the best classification was obtained by XGBOOST combined with SMOTE-TOMEK. Furthermore, this paper used the NARX network to track the changes generated by time-based indicators, providing another insight to refine the study of intelligent diagnosis of Alzheimer.