{"title":"基于人工神经网络的糖尿病分类与预测","authors":"Kshitij Tripathi","doi":"10.34218/IJCET.10.3.2019.018","DOIUrl":null,"url":null,"abstract":"The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"DIABETES CLASSIFICATION AND PREDICTION USING ARTIFICIAL NEURAL NETWORK\",\"authors\":\"Kshitij Tripathi\",\"doi\":\"10.34218/IJCET.10.3.2019.018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.\",\"PeriodicalId\":38492,\"journal\":{\"name\":\"International Journal of Computer Aided Engineering and Technology\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer Aided Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34218/IJCET.10.3.2019.018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Aided Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34218/IJCET.10.3.2019.018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
DIABETES CLASSIFICATION AND PREDICTION USING ARTIFICIAL NEURAL NETWORK
The classification of data is an important field of data mining comes under supervised learning. In this approach classifier is trained on the pre-categorized data thereafter tested on unseen part called test data to evaluate it. The other related field clustering comes under unsupervised learning is used for categorizing data into different clusters or assigning labels to them which are previously unknown. In this article the classification of data is done and we are using artificial neural networks (ANN) for pre-processing i.e. removing noisy instances through novel clustering technique and then classifying pre-processed data through ANN. Both are exhaustive approaches. The data set used in this article is PIMA Indian diabetes data set available on UCI repository.
期刊介绍:
IJCAET is a journal of new knowledge, reporting research and applications which highlight the opportunities and limitations of computer aided engineering and technology in today''s lifecycle-oriented, knowledge-based era of production. Contributions that deal with both academic research and industrial practices are included. IJCAET is designed to be a multi-disciplinary, fully refereed and international journal.