Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

Olawumi OLASUNKANMİ; Odunayo OLANLOYE; Abdulquadri ADEGBİJİ

doi:https://doi.org/10.18100/ijamec.792863

Makale detayları

Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

Yazarlar:

Olawumi OLASUNKANMİ | Odunayo OLANLOYE | Abdulquadri ADEGBİJİ

Yayıncı:

Selcuk University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Applied Mathematics Electronics and Computers 2020 Vol.8 Issue 4, pp.197-202

Belge Tipi:

Makale

Yayın Tarihi:

12/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000475726

Sıklık:

Üç Aylık

eISSN:

2147-8228

pISSN:

2147-8228

DOI:

https://doi.org/10.18100/ijamec.792863

Özet:

 

The world is now in the era of big data and processing, and exploring the data has become one of the significant challenges. Hence, researchers have done a lot to analyse these data in the health sector to enhance disease detection and classification using artificial intelligence and ML principles. Kidney disease is one of the terrible conditions in which its late detection has sent many people to untimely graves. ML classifiers have been employed in many dimensions to classify heart disease, but, existing works have not explored the variants of each method for selection of best model parameters. An attempt is being made in this research to study the behaviour of three (3) variants each from two(2) tree-based models in the classification of Kidney Disease. Three of the variants are Complex, Medium and Simple models of Decision tree classifier and the other one are Boosted, Bagged and RUSBoosted of Ensemble Classifiers. Using MATLAB for implementation, the model performance established that the accuracy of Ensemble Classifier (Bagged tree model) is the best, concerning the speed, Decision tree (Complex and Simple tree models have the same and highest value). Hence, the two are the best. In terms of training time, Decision tree(Simple tree) has the least time and therefore the best.

Makale detayları

Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

Yazarlar: Olawumi OLASUNKANMİ | Odunayo OLANLOYE | Abdulquadri ADEGBİJİ

Yayıncı: Selcuk University

Ana Başlıklar: Uygulamalı fizik

Dergi: International Journal of Applied Mathematics Electronics and Computers 2020 Vol.8 Issue 4, pp.197-202

Belge Tipi: Makale

Yayın Tarihi: 12/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000475726

Sıklık: Üç Aylık

eISSN: 2147-8228

pISSN: 2147-8228

DOI: https://doi.org/10.18100/ijamec.792863

Sayı

Yazarlar:

Olawumi OLASUNKANMİ | Odunayo OLANLOYE | Abdulquadri ADEGBİJİ

Yayıncı:

Selcuk University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Applied Mathematics Electronics and Computers 2020 Vol.8 Issue 4, pp.197-202

Belge Tipi:

Makale

Yayın Tarihi:

12/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000475726

Sıklık:

Üç Aylık

eISSN:

2147-8228

pISSN:

2147-8228

DOI:

https://doi.org/10.18100/ijamec.792863