Publication:
Analysing the performance of classification algorithms on diseases datasets

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dc.citedby5
dc.contributor.authorLydia E.L.en_US
dc.contributor.authorSharmil N.en_US
dc.contributor.authorShankar K.en_US
dc.contributor.authorMaseleno A.en_US
dc.contributor.authorid57196059278en_US
dc.contributor.authorid57191575400en_US
dc.contributor.authorid56884031900en_US
dc.contributor.authorid55354910900en_US
dc.date.accessioned2023-05-29T07:29:10Z
dc.date.available2023-05-29T07:29:10Z
dc.date.issued2019
dc.description.abstractChange in regular food habits and physical activities of the human body, some of the genetic diseases were inherited from generation to generation. The most common hereditary diseases that stay lifetime are thyroid, diabetics, cancer. Predicting cancer-like diseases consumes time; cure for such hereditary diseases can be identified at an early stage. Medical technology has been improved for the prognosis of healthcare. Healthcare using prediction analysis enhances medical technology. Researchers have advanced Prediction modelling under three phases. In the first state, they define the issue, collection of data and progress the data. In the second state, they choose a model and perform training and testing and in the third state, they apply the model in real-world. This has become a crucial task in the medical field for immediate disease diagnosis. To advance such automatic healthcare prediction system, modern Artificial Intelligent technology has been developed an easy way to identify the existence of the diseases. The proposed research papers examine the diseases through the disease parameters and classify them using various developed intense classification algorithms such as Support Vector Machine, Decision tree, Logistic Regression, K-nearest neighbor, Naive Bayes. The proposed classification algorithms measure the diseases using the disease datasets which estimates the accurate prediction. The experimental analyses have been carried out over three disease datasets namely Thyroid dataset, diabetics data set, cancer dataset. � 2019, Research Trend. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage230
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85073725231
dc.identifier.spage224
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073725231&partnerID=40&md5=51b356873fc545adaf1ccd2f846ba8ce
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24938
dc.identifier.volume10
dc.publisherResearch Trenden_US
dc.sourceScopus
dc.sourcetitleInternational Journal on Emerging Technologies
dc.titleAnalysing the performance of classification algorithms on diseases datasetsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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