综合分析基于神经网络的甲状腺疾病预测模型

Anu K.P., J. B. Benifa
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引用次数: 1

摘要

如今,数据分析在构建机器学习模型中起着重要的作用,特别是在医疗数据分析的情况下。统计分析工具帮助我们分析大量数据,也用于识别数据集中的共同趋势和模式。这种统计分析可以用来将大数据转化为有意义的信息。就医疗数据集而言,主要的问题是数据表示不一致,例如在诊断后,一些医学专家会将性别表示为F和M,而另一些医学专家会将性别表示为1和0,有时同一位专家会使用不同的格式来表示相同的性别,因此数据预处理在这里具有重要作用。对于医疗数据集的统计分析,使用了一些python工具。本文使用甲状腺医学数据集进行统计分析。经过统计分析,将该数据集传递给深度学习神经网络,准确率为99.07%,f1分数为93.69%,召回率为89.66%,精密度为98.11%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comprehensive analysis using neural network-based model for thyroid disease prediction
Nowadays data analysis has an important role in building a machine-learning model, especially in the case of medical data analysis. Statistical analysis tools help us to analyze large amounts of data and are also used to identify common trends and patterns in the dataset. This statistical analysis can be used to convert big data into meaningful information. In the case of medical datasets, the major issue is inconsistent data representation, for example, after diagnosis, some medical experts will represent the gender as F and M for male and female some others will represent it as 1 and 0, and sometimes the same expert will use a different format for the same gender representation, so the data pre-processing has an important role here. For the statistical analysis of the medical dataset, some python tools are used. Here thyroid medical datasets are used for the statistical analysis. After statistical analysis, this dataset is passed over to a deep learning neural network and got an accuracy of 99.07%, F1-score of 93.69%, Recall of 89.66%, and Precision of 98.11%.
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