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MLTDD : Use of Machine Learning Techniques for Diagnosis of Thyroid Gland Disorder

机译:MLTDD:使用机器学习技术诊断甲状腺疾病

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Machine learning algorithms are used to diagnosis for many diseases after very importantimprovements of classification algorithms as well as having large data sets and high performingcomputational units. All of these increased the accuracy of these methods. The diagnosis ofthyroid gland disorders is one of the application for important classification problem. Thisstudy majorly focuses on thyroid gland medical diseases caused by underactive or overactivethyroid glands. The dataset used for the study was taken from UCI repository. Classification ofthis thyroid disease dataset was a considerable task using decision tree algorithm. The overallprediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. Inthis study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD),an Intelligent thyroid gland disease prediction tool in Python, which can effectively help tomake the right decision, has been designed using PyDev, which is python IDE for Eclipse.
机译:在对分类算法进行了非常重要的改进之后,机器学习算法被用于诊断许多疾病,并且具有大数据集和高性能计算单元。所有这些都提高了这些方法的准确性。甲状腺疾病的诊断是重要分类问题的应用之一。本研究主要研究由甲状腺活动不足或过度活跃引起的甲状腺医学疾病。用于研究的数据集取自UCI存储库。使用决策树算法对该甲状腺疾病数据集进行分类是一项艰巨的任务。训练的整体预测准确性为100%,测试的整体预测准确性为98.7%至99.8%。在这项研究中,我们使用PyDev(适用于Eclipse的python IDE)设计了用于Python的智能甲状腺疾病预测工具,它可以有效帮助做出正确的决策,从而开发了用于甲状腺疾病诊断的机器学习工具(MLTDD)。

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