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Intelligent Syncope Disease prediction framework using DM-ensemble techniques

机译:使用DM集成技术的智能Syncope疾病预测框架

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摘要

Data mining can be used in various fields' i.e. mobile computing, web mining, expert predictions, crime analysis, engineering, management and medicine. In medical field, data mining techniques can be used by the researchers for the diagnosis and prediction of various diseases. A framework is proposed to predict Syncope Disease using Ensemble technique that contains Naïve Bayes, Gini Index and Support Vector Machine classifiers. Patient's data set for this research work is obtained from Armed Forces Institute of Cardiology (AFIC & NIHD) in Pakistan. Thirty one attributes have been used to predict Syncope using Ensemble techniques but each technique uses its own way to predict Syncope based on specific rules. In the end results are compared and accuracy is measured on majority voting from applied data mining ensemble techniques. Results prove that proposed research framework is accurate and can be used for future development.
机译:数据挖掘可以用于各个领域,即移动计算,网络挖掘,专家预测,犯罪分析,工程,管理和医学。在医学领域,研究人员可以使用数据挖掘技术来诊断和预测各种疾病。提出了一个使用Ensemble技术预测Syncope疾病的框架,该框架包含朴素贝叶斯,基尼系数和支持向量机分类器。从巴基斯坦武装部队心脏病研究所(AFIC&NIHD)获得了该研究工作的患者数据集。已经使用Ensemble技术使用了31个属性来预测Syncope,但是每种技术都使用其自身的方法根据特定规则来预测Syncope。最后,对结果进行了比较,并从应用的数据挖掘集成技术中对多数投票进行了准确性评估。结果证明所提出的研究框架是准确的,可用于未来的发展。

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