{"title":"Smart heart disease prediction system using Improved K-means and ID3 on big data","authors":"Tejaswini U. Mane","doi":"10.1109/ICDMAI.2017.8073517","DOIUrl":null,"url":null,"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.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMAI.2017.8073517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.