Comparative Analysis of Some Prominent Machine Learning Algorithm for the Prediction of Chronic Kidney Disease

Authors

  • Iliyas, I.I. Department of Mathematical Science, University of Maiduguri
  • Isah, R.S. Department of Mathematical Science, University of Maiduguri
  • Ali, B.D. Department of Mathematical Science, University of Maiduguri
  • Andra, U. Department of Mathematical Science, University of Maiduguri

Keywords:

Chronic Kidney Disease, Machine Learning, Deep Neural Network, Artificial Neural Network, Naives Bayes, Logistics Regression, K-Neighbor Nearest

Abstract

Chronic Kidney Disease (CKD) is a disorder against proper function regarding kidneys, as kidneys filter our blood whenever CKD gets worse, our blood receives wastes at a higher level, which results in sickness. It also has a substantial financial problem for families of subjects with a medical issue in Nigeria. Among the necessary measures that need action concerning the increase of CKD is detecting the disease early and with different data mining techniques. Data mining is gradually becoming more prevalent nowadays in healthcare, as also in fraud, abuse detection etc. Classification is a more useful data mining function to handle items in a collection to class or target categories. For obtaining essential information from medical database, machine learning and statistical analysis can assist in extracting hidden patterns and identify relationships from vast among of data. In this study, we compared five (5) different
models namely: Deep Neural Network (DNN), Artificial Neural Network (ANN), Naïve Bayes (NB), Logistic Regression (LR), and K-Neighbor Nearest (KNN) to predict CKD on Gashua General Hospital (GGH) dataset. The study achieved an accuracy of 98% for DNN, KNN: 96%, NB: 97%, LR: 96% and ANN: 96%. 

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Published

2021-12-01

How to Cite

Iliyas, I.I., Isah, R.S., Ali, B.D., & Andra, U. (2021). Comparative Analysis of Some Prominent Machine Learning Algorithm for the Prediction of Chronic Kidney Disease. Academy Journal of Science and Engineering, 15(1), 132–150. Retrieved from https://ajse.academyjsekad.edu.ng/index.php/new-ajse/article/view/39