利用童年创伤、心理健康问卷和机器学习级联集成预测印度成年人的自杀行为
Predicting suicidal behavior among Indian adults using childhood trauma, mental health questionnaires and machine learning cascade ensembles
AI总结：
本研究针对印度缺乏自杀行为预测机器学习研究的现状，基于391名印度成年人的童年创伤与心理健康问卷数据，构建级联集成学习模型，实现95.04%的自杀行为分类准确率，为针对性干预提供支持。
摘要：
在年轻成年人中，自杀是印度首要的死亡原因，其全国自杀率约达16%，令人警醒。近年来，机器学习算法被用于通过各类行为特征预测自杀行为，但迄今为止，尚无文献探讨机器学习算法在印度语境下预测自杀行为的效能。本研究开发了多种机器学习算法与集成模型，基于童年创伤、不同心理健康参数及其他行为因素预测自杀行为。数据集取自印度一家健康中心的391名个体，通过标准化问卷收集了他们的童年创伤、心理健康状况及其他心理问题相关信息。结果显示，结合支持向量机、决策树和随机森林的级联集成学习方法，利用童年创伤与心理健康问卷数据对自杀行为进行分类的准确率可达95.04%。本研究凸显了运用这类机器学习集成模型识别有自杀倾向个体的潜力，从而可高效提供针对性干预措施。
英文摘要：
Among young adults, suicide is India's leading cause of death, accounting for an alarming national suicide rate of around 16%. In recent years, machine learning algorithms have emerged to predict suicidal behavior using various behavioral traits. But to date, the efficacy of machine learning algorithms in predicting suicidal behavior in the Indian context has not been explored in literature. In this study, different machine learning algorithms and ensembles were developed to predict suicide behavior based on childhood trauma, different mental health parameters, and other behavioral factors. The dataset was acquired from 391 individuals from a wellness center in India. Information regarding their childhood trauma, psychological wellness, and other mental health issues was acquired through standardized questionnaires. Results revealed that cascade ensemble learning methods using a support vector machine, decision trees, and random forest were able to classify suicidal behavior with an accuracy of 95.04% using data from childhood trauma and mental health questionnaires. The study highlights the potential of using these machine learning ensembles to identify individuals with suicidal tendencies so that targeted interinterventions could be provided efficiently.
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。
