Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2197
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dc.contributor.authorGunarathne, W. H. S. D-
dc.contributor.authorPerera, K. D. M-
dc.contributor.authorKahandawaarachchi, K. A. D. C. P-
dc.date.accessioned2022-05-04T03:51:04Z-
dc.date.available2022-05-04T03:51:04Z-
dc.date.issued2017-10-23-
dc.identifier.citationW. H. S. D. Gunarathne, K. D. M. Perera and K. A. D. C. P. Kahandawaarachchi, "Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD)," 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), 2017, pp. 291-296, doi: 10.1109/BIBE.2017.00-39.en_US
dc.identifier.issn2471-7819-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2197-
dc.description.abstractChronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2017 IEEE 17th international conference on bioinformatics and bioengineering (BIBE);Pages 291-296-
dc.subjectPerformance Evaluationen_US
dc.subjectMachine Learningen_US
dc.subjectClassification Techniquesen_US
dc.subjectDisease Classificationen_US
dc.subjectForecastingen_US
dc.subjectData Analyticsen_US
dc.subjectChronic Kidney Diseaseen_US
dc.titlePerformance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD)en_US
dc.typeArticleen_US
dc.identifier.doi10.1109/BIBE.2017.00-39en_US
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications



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