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

机译:基于遗传算法的智能数据挖掘与机器学习用于心理健康诊断

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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:对精神健康疾病的不正确诊断会导致错误的治疗,并导致服务对象的心理健康状况不可逆转地恶化,包括住院和/或过早死亡。在美国,每年大约有1200万患者被误诊。在本文中,一项新颖的研究引入了一种半自动化系统,该系统可帮助对心理障碍患者进行初步诊断。这是根据患者心理健康状况的描述与DSM-IV-TR,第四版文本修订版中说明的精神疾病的匹配来完成的。该研究基于遗传算法,分类数据挖掘和机器学习技术的集成,构建了半自动化系统。目的不是要完全自动化精神病患者的分类过程,而是要确保分类者了解所有可能与患者症状相符的精神健康疾病。分类者/心理分析者将能够进行有根据的,明智的和适当的评估,从而可以得出准确的预后。分析人员将是诊断和治疗计划的最终选择者。

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