慢性肾病数据分析的智能预测技术

V. Shanmugarajeshwari, M. Ilayaraja
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引用次数: 0

摘要

信息存储在各个领域,如金融、银行、医院、教育等。目前,存储在医疗数据库中的数据增长迅速。所提出的方法包含三个部分,与预处理、属性选择和分类C5.0算法相当。这项工作的目的是设计一个基于机器的诊断方法,使用各种技术。这些算法提高了慢性肾脏疾病危险因素挖掘的效率,但也存在一些不足。为了克服这些问题,完善一个有效的临床决策支持系统,在大量数据集上使用分类方法进行更好的决策和预测,本文通过使用C5.0算法进行分组分类组装,指向组装时间,通过分析慢性肾脏疾病数据集,获得较高的准确性,以识别早期诊断的慢性肾脏疾病患者的风险水平。
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Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis
Information is stored in various domains like finance, banking, hospital, education, etc. Nowadays, data stored in medical databases are growing rapidly. The proposed approach entails three parts comparable to preprocessing, attribute selection, and classification C5.0 algorithms. This work aims to design a machine-based diagnostic approach using various techniques. These algorithms improve the efficiency of mining risk factors of chronic kidney diseases, but there are also have some shortcomings. To overcome these issues and improve an effectual clinical decision support system exhausting classification methods over a large volume of the dataset for making better decisions and predictions, this paper presents grouping classification assembly through consuming the C5.0 algorithm, pointing towards assembling time to acquire great accuracy to identify an early diagnosis of chronic kidney disease patients with risk level by analyzing the chronic kidney disease dataset.
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