Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD)

Chronic 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 imp...

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Vydáno v:Proceedings / Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE) s. 291 - 296
Hlavní autoři: Gunarathne, W. H. S. D., Perera, K. D. M., Kahandawaarachchi, K. A. D. C. P.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2017
Témata:
Chronic-Kidney-Disease;-symptoms;-predictive-models;-machine-learning;-classification-algorithms
Classification algorithms
Data mining
Diseases
Kidney
Logistics
Prediction algorithms
Predictive models
ISSN:2471-7819
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Shrnutí:Chronic 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.
ISSN:2471-7819
DOI:10.1109/BIBE.2017.00-39