Abstract
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.
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Zhou, Y., Guan, Z., Ji, P., Wang, X. (2020). Drug Abuse Prediction Model Based on Relevance Analysis. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_83
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DOI: https://doi.org/10.1007/978-981-15-1468-5_83
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Online ISBN: 978-981-15-1468-5
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