Suicide Prediction Analysis with Generalized Addictive Model The issue of suicide rate has become increasingly rigorous and has received extensive attention in the contemporary society. In order to explore the pattern of suicide rate variation and intrinsic incentive for suicide, the research was conducted to analyze the data involved multifaceted factors since the suicide is rarely caused by only one factor. Establishing statistical models like xe2x80x9cGeneralized Additive Modelxe2x80x9d provides a conceptual framework for the study and is instrumental in predicting the suicide rate in 2017. Based on the past data from year 1985 to 2016, the prediction of suicide rate in 2017 is presented specifically. Multiple invisible results are revealed including the negative linear relationship between GDP and suicide rate, the positive correlation with age and the uneven distribution worldwide. These results presented with functions and graphs show how financial, cultural or regional factors might affect the suicide rate in different areas. The prediction are still affected by factors difficult to estimate, such as socioeconomic relations. However, the results are sufficient to help understand the suicide issue by analyzing itu0027s origin and distribution.

Drug Abuse Prediction Model Based on Relevance Analysis Based on the data sets of drug use and socioeconomic data from five states in the United States, this paper proposes a prediction model based on correlation analysis. Firstly, the indicators are screened according to the correlation degree, then the degree of drug abuse and the selected indicators are predicted by multiple regression. Secondly, the weighted polynomial regression algorithm is used to predict the degree of drug abuse separately. Finally, the Lagrange multiplier method is used to assign the optimal weight to the two prediction results. The experimental results show that the prediction effect of the model is much better than that of the single prediction result of the target index.