基于深度神经网络的心脏病诊断数据挖掘方法

IF 1.2 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
A. A. Romalt, Mathusoothana S. Kumar
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引用次数: 1

摘要

使用机器学习和数据挖掘方法预测和检测心脏病在临床上非常有用,进一步的进展将相当困难。通过使用数字患者数据为世界上缺乏心血管知识和错误诊断数量高的国家的临床决策提供分析支持,可以改善心脏病早期预测。研究中使用了许多有监督的机器学习算法来寻找预测心脏病准确率最高的分类器。他们还根据他们的表现和准确性受到歧视。该研究的目标是利用人工智能(AI)在正常和病理情况下诊断心脏病。预计将使用各种人工智能技术,其中DNN(深度神经网络)的表现优于其他技术。这是通过分析预测的,一种更新的蜘蛛猴优化(USMO)技术已经被提出用于深度神经网络作为确定最优权重的手段。调查结果显示,克利夫兰数据库的准确率为96.77%,匈牙利数据库的准确率为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Mining Approach for Diagnosing Heart Diseases through Deep Neural Network
Predicting and detecting cardiac illness using machine learning and data mining approaches is extremely clinically useful, and further progress will be quite difficult. Heart disease early-stage prediction can be improved by using digital patient data to provide analytical support for clinical decision-making in countries throughout the world where cardiovascular knowledge is lacking and the number of erroneous diagnoses is high. Many supervised machine-learning algorithms were utilised in the study to find classifiers with the highest accuracy in predicting heart disease. They were also discriminated based on their performance and accuracy. The goal of the study is to use AI (Artificial Intelligence) to diagnose cardiac disease in both normal and pathological settings. Various AI technologies are expected to be used, with DNN (Deep Neural Network) outperforming the others. This is predicted by the analysis an updated spider monkey optimization (USMO) technique has been proposed for the DNN as a means of determining optimal weights. The investigation's findings reveal a precision of 96.77 percent in the Cleveland database and a precision of 100 percent in the Hungarian database.
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来源期刊
Periodico Di Mineralogia
Periodico Di Mineralogia 地学-地球化学与地球物理
CiteScore
1.50
自引率
14.30%
发文量
0
审稿时长
>12 weeks
期刊介绍: Periodico di Mineralogia is an international peer-reviewed Open Access journal publishing Research Articles, Letters and Reviews in Mineralogy, Crystallography, Geochemistry, Ore Deposits, Petrology, Volcanology and applied topics on Environment, Archaeometry and Cultural Heritage. The journal aims at encouraging scientists to publish their experimental and theoretical results in as much detail as possible. Accordingly, there is no restriction on article length. Additional data may be hosted on the web sites as Supplementary Information. The journal does not have article submission and processing charges. Colour is free of charges both on line and printed and no Open Access fees are requested. Short publication time is assured. Periodico di Mineralogia is property of Sapienza Università di Roma and is published, both online and printed, three times a year.
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