GA-SVM用于海洛因消费风险评估
GA-SVM for Evaluating Heroin Consumption Risk
摘要：
2017年，美国有超过70，000例药物过量死亡。其中近一半涉及使用海洛因等类阿片。这项研究通过进一步了解导致海洛因消费的因素来支持打击阿片类药物流行病的努力。以前的研究一直在争论海洛因成瘾的原因，一些人将这种现象解释为处方阿片类药物的过渡，另一些人则指出各种心理社会因素。本研究使用自我报告的人格，人口统计学和毒品消费行为的信息来预测海洛因消费。通过应用遗传算法优化的支持向量机算法（GA-SVM Hybrid）同时识别预测特征和模型参数，本研究产生了几个模型，这些模型在预测海洛因使用方面比以前的研究中产生的模型更准确。虽然所有因素都有预测能力，但这些结果表明，其他药物（处方药和非法药物）的消费比心理社会因素更能预测海洛因的使用。使用处方药作为海洛因使用的一个强有力的预测因素是一个重要的，虽然令人不安的发现，但可以帮助打击海洛因的使用。
英文摘要：
There were over 70,000 drug overdose deaths in the USA in 2017. Almost half of those involved the use of Opioids such as Heroin. This research supports efforts to combat the Opioid Epidemic by further understanding factors that lead to Heroin consumption. Previous research has debated the cause of Heroin addiction, with some explaining the phenomenon as a transition from prescription Opioids, and others pointing to various psycho-social factors. This research used self-reported information about personality, demographics and drug consumption behavior to predict Heroin consumption. By applying a Support Vector Machine algorithm optimized with a Genetic Algorithm (GA-SVM Hybrid) to simultaneously identify predictive features and model parameters, this research produced several models that were more accurate in predicting Heroin use than those produced in previous studies. Although all factors had predictive power, these results showed that consumption of other drugs (both prescription and illicit) were stronger predictors of Heroin use than psycho-social factors. The use of prescription drugs as a strong predictor of Heroin use is an important though disturbing discovery but that can help combat Heroin use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
