基于人工神经网络的糖尿病分类与预测

Kshitij Tripathi
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引用次数: 1

摘要

数据分类是数据挖掘的一个重要领域,属于监督学习范畴。在这种方法中,分类器在预先分类的数据上进行训练,然后在未见的部分(称为测试数据)上进行测试以对其进行评估。另一个相关领域聚类属于无监督学习,用于将数据分类到不同的聚类或为它们分配以前未知的标签。本文对数据进行了分类,并使用人工神经网络进行预处理,即通过新颖的聚类技术去除噪声实例,然后通过人工神经网络对预处理后的数据进行分类。两者都是详尽的方法。本文中使用的数据集是UCI存储库中提供的PIMA印度糖尿病数据集。
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DIABETES CLASSIFICATION AND PREDICTION USING ARTIFICIAL NEURAL NETWORK
The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
90
期刊介绍: IJCAET is a journal of new knowledge, reporting research and applications which highlight the opportunities and limitations of computer aided engineering and technology in today''s lifecycle-oriented, knowledge-based era of production. Contributions that deal with both academic research and industrial practices are included. IJCAET is designed to be a multi-disciplinary, fully refereed and international journal.
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