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Predicting Diabetes by adopting Classification Approach in Data Mining

机译:通过采用数据挖掘中的分类方法来预测糖尿病

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As the world is growing fast, the metamorphosing of things, lifestyle, perceptions of people and resources is taking place. But the elevation in technology has become a challenge now as the ideas, innovations are amplifying. One of the biggest things the advancement and elevations in technology has given birth is “Big Data”. In this data massive amount of information is hidden. In order to refine or process this data and to find out and unmask the insights, many techniques and algorithms have been evolved, one of which is the data mining. The data mining is the approach or procedure which helps in detaching or extracting profitable and fruitful knowledge, reports and facts from the rough or impure data. The prediction analysis is approach comprehended from data mining to forecast and figure out the future making using classification technique. This research work is based on the diabetes prediction by making use of classification approach. In the existing approach SVM classifier is applied for the prediction analysis. To increase accuracy approach of KNN classifier is applied for the prediction analysis. Both the proposed and existing methods are implemented in Python. The simulation results show that accuracy of KNN is increased and execution time is reduced.
机译:随着世界上的快速增长,事情变色,生活方式,对人和资源的看法正在发生。但是技术的海拔已经成为现在的挑战,作为思想,创新正在放大。技术进步和高度的最大事物之一给出了出生的是“大数据”。在此数据中,隐藏了大量信息。为了改进或处理此数据并找出和揭示洞察力,许多技术和算法已经发展,其中一个是数据挖掘。数据挖掘是一种方法或程序,有助于分离或提取粗略或不纯数据的盈利和富有成效的知识,报告和事实。预测分析是从数据挖掘到预测和使用分类技术的未来制作的方法。本研究工作基于利用分类方法基于糖尿病预测。在现有的方法中,SVM分类器应用于预测分析。为了提高KNN分类器的精度方法,应用了预测分析。建议和现有方法都在Python中实现。仿真结果表明,kNN的准确性增加,减少了执行时间。

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