Smart heart disease prediction system using Improved K-means and ID3 on big data

Tejaswini U. Mane
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引用次数: 25

Abstract

The term Big Data is becoming global today. The Big data is huge amount of variety of data, and the data is increasing very rapidly according to the time. So there is need to process that data and instead of just storing that data need to extract some meaningful information or knowledge from that data applying some clustering and classification techniques of data mining. There are various era available in the Big Data so that decided the medical field first. And after that there are various diseases available to work on them or gain some knowledge or predict for help we decided the Heart disease. Heart disease is one of the disease due to that death will occurred mostly, and according to the world health organization the percentage is more for that. So Heart disease is decided for the big Data approach, and as Big Data is considered so used Hadoop Map reduce platform. For clustering Improved K-Means and for the classification purpose decision tree algorithm i.e. ID3 is used in the hybrid approach. As we know the taking second opinion is too increased, the system is very useful for the helping in prediction, basis on the some parameters like chest pain, cholesterol, age, resting Bp, Thalac and many more. Due to this system clinical decision making will be improved as well as being fast. It's also will impact on the improving the treatment process. In such way it will be very useful in the prediction of the heart disease.
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基于大数据改进K-means和ID3的智能心脏病预测系统
如今,“大数据”一词正变得全球化。大数据是海量的各种数据,并且随着时间的推移,数据的增长速度非常快。因此,我们需要处理这些数据,而不仅仅是存储这些数据,我们需要从这些数据中提取一些有意义的信息或知识,应用一些数据挖掘的聚类和分类技术。在大数据中有各种各样的时代,所以决定了医疗领域首先。在那之后,有各种各样的疾病可供研究或获得一些知识或预测帮助我们决定心脏病。心脏病是导致死亡最多的疾病之一,根据世界卫生组织的数据,这一比例更高。所以心脏病决定采用大数据的方法,而由于大数据的考虑所以采用了Hadoop Map reduce平台。在混合方法中使用改进的K-Means聚类算法和ID3决策树算法进行分类。正如我们所知,采取第二意见太增加,该系统是非常有用的帮助预测,基于胸痛,胆固醇,年龄,静息血压,Thalac等参数。由于该系统,临床决策将得到改善和快速。这也会对改善治疗过程产生影响。这样,它对心脏病的预测将是非常有用的。
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