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Intelligent data mining and machine learning for mental health diagnosis using genetic algorithm

机译:遗传算法智能数据挖掘与机器学习心理健康诊断

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Inappropriate diagnosis of mental health illnesses leads to wrong treatment and causes irreversible deterioration in the client's mental health status including hospitalization and/or premature death. About 12 million patients are misdiagnosed annually in US. In this paper, a novel study introduces a semi-automated system that aids in preliminary diagnosis of the psychological disorder patient. This is accomplished based on matching description of a patient's mental health status with the mental illnesses illustrated in DSM-IV-TR, Fourth Edition Text Revision. The study constructs the semi-automated system based on an integration of the technology of genetic algorithm, classification data mining and machine learning. The goal is not to fully automate the classification process of mentally ill individuals, but to ensure that a classifier is aware of all possible mental health illnesses could match patient's symptoms. The classifier/psychological analyst will be able to make an informed, intelligent and appropriate assessment that will lead to an accurate prognosis. The analyst will be the ultimate selector of the diagnosis and treatment plan.
机译:心理健康疾病的不适当诊断导致错误的治疗,并导致客户心理健康状况不可逆转的恶化,包括住院和/或过早死亡。大约1200万名患者每年在美国误诊。在本文中,新型研究介绍了一种半自动系统,有助于心理障碍患者的初步诊断。这基于患者心理健康状况与DSM-IV-TR,第四版文本修订中所示的精神疾病的匹配描述来实现。该研究基于集成遗传算法技术,分类数据挖掘和机器学习的集成构建了半自动系统。目标不是充分自动化精神病患者的分类过程,而是确保分类器意识到所有可能的精神健康疾病可以匹配患者的症状。分类器/心理学分析师将能够做出明智,智能和适当的评估,将导致准确的预后。分析师将成为诊断和治疗计划的终极选择器。

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