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Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector
Mobile Networks and Applications ( IF 3.8 ) Pub Date : 2020-05-29 , DOI: 10.1007/s11036-020-01549-9
A. H. Zubar , R. Balamurugan

Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized optimization techniques in big data analytics consider the important part of the healthcare network communication issues in decision making approach of patient information. This article focused on heart disease data mining and relevant issues since the heart diseases are considered as a reason for causing deaths just as for males and females all over the world. So, people need to be conscious of possible aspects of heart disease. Even though genetics has a part, some of the standards of living practiced are the fundamental reasons for the heart disease. The heart diseases are classified by classical techniques with 13 risk factors and helpful variables. The introduced approach delivers a new computing hybrid modeling scheme for detect the heart diseases. This study represents, various existing methods making decisions for cardio vascular risks depends on the artificial neural networks (ANN). This ANN based methods generally anticipated that Heart Failure attributes having same risk involvement to the heart failure diagnosis. In this article the strategy of an effective recognition method is analyzed for analyzing the failure related to heart diseases using a hybridized approach of K-Nearest Neighbor clustering and Spiral optimization in the classification of the cardio vascular risks. The hybridized KNN technique is matched with some data mining techniques like Support vector Machine (SVM), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). The experimental results of this work achieved optimized improved results significantly than other machine learning techniques. The illustrative results exposed that the hybrid scheme stated effectually classify heart disease in the way of computing optimized prediction of heart diseases. Overall the proposed algorithm evidence 5% of enhancement in prediction of heart diseases with comparison of other existing machine learning techniques.

中文翻译:

医疗领域的绿色计算过程及其使用机器学习算法的优化

处理信息是医疗保健部门的关键任务;数据挖掘技术将是解决复杂问题的正确选择。大数据分析中的混合优化技术在患者信息决策方法中考虑了医疗网络通信问题的重要部分。由于心脏病被认为是导致死亡的原因,与全世界的男性和女性一样,本文重点讨论了心脏病数据的挖掘和相关问题。因此,人们需要意识到心脏病的可能方面。尽管遗传因素很重要,但实践中的某些生活水平却是导致心脏病的根本原因。通过经典技术将心脏疾病分类为13个危险因素和有用变量。引入的方法提供了一种用于检测心脏病的新的计算混合建模方案。这项研究表明,各种决策心血管风险的现有方法取决于人工神经网络(ANN)。这种基于ANN的方法通常可以预期,心力衰竭的属性与心力衰竭的诊断具有相同的风险。本文分析了一种有效识别方法的策略,该方法使用K最近邻聚类和螺旋优化的混合方法对心血管疾病的分类进行分析,以分析与心脏病相关的故障。混合KNN技术与某些数据挖掘技术相匹配,例如支持向量机(SVM),卷积神经网络(CNN)和人工神经网络(ANN)。与其他机器学习技术相比,这项工作的实验结果获得了优化的改进结果。说明性结果表明,混合方案表示可以通过计算心脏病的最佳预测方式有效地对心脏病进行分类。总体而言,与其他现有的机器学习技术相比,该算法证明了心脏病预测能力提高了5%。
更新日期:2020-05-29
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