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Big data management with machine learning inscribed by domain knowledge for health care

机译:通过领域知识题写的机器学习大数据管理

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摘要

In this work, a framework that helps in the disease diagnosis process with big-data management and machine learning using rule based, instance based, statistical, neural network and support vector method is given. Concerning this, big-data that contains the details of various diseases are collected, preprocessed and managed for classification. Diagnosis is a day-to-day activity for the medical practitioners and is also a decision-making task that requires domain knowledge and expertise in the specific field. This framework suggests different machine learning methods to aid the practitioner to diagnose disease based on the best classifier that is identified in the health care system. The framework has three main segments like big-data management, machine learning and input/output details of the patient. It has been already proved in the literature that the computing methods do help in disease diagnosis, provided the data about that particular disease is available in the data center. Thus this framework will provide a source of confidence and satisfaction to the doctors, as the model generated is based on the accuracy of the classifier compared to other classifiers.
机译:在这项工作中,提出了一个框架,该框架使用基于规则,基于实例,统计,神经网络和支持向量法的大数据管理和机器学习来帮助疾病诊断过程。因此,收集,预处理和管理包含各种疾病详细信息的大数据以进行分类。诊断是医疗从业人员的日常活动,也是一项决策任务,需要特定领域的领域知识和专业知识。该框架提出了不同的机器学习方法,以帮助从业者根据卫生保健系统中确定的最佳分类器来诊断疾病。该框架具有三个主要部分,例如大数据管理,机器学习和患者的输入/输出详细信息。文献已经证明,只要有关特定疾病的数据在数据中心中可用,则计算方法确实有助于疾病诊断。因此,该框架将为医生提供信心和满意度,因为生成的模型是基于与其他分类器相比分类器的准确性。

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