Two-class classification: comparative experiments for chronic kidney disease
Over two million of population across worldwide is currently depending on dialysis treatment or a kidney transplant to survive from kidney disease. Therefore, it is imperative for health agencies such as hospitals or insurance companies to predict the probabilities of patients who suffers from c...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/5138/ http://eprints.uthm.edu.my/5138/1/KP%202020%20%28101%29.pdf |
| Summary: | Over two million of population across worldwide is
currently depending on dialysis treatment or a kidney transplant
to survive from kidney disease. Therefore, it is imperative for
health agencies such as hospitals or insurance companies to
predict the probabilities of patients who suffers from chronic case
of kidney diseases, hence requiring medical attentions. This study
performs a comparative experiment on prediction of chronic
kidney disease via a classification methodology. Two supervised
classification algorithms are used to build the classification model,
which are Two-Class Decision Forest and Two-Class Neural
Networks. Experimental results showed that Neural Network
performed better based on all features but Decision Forest
produced optimal performance with high accuracy, and precision
as compared to Neural Networks and other algorithms from the
literature such as K-Nearest Neighbor, Support Vector Machine,
and Rule Induction. |
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