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Application of Machine Learning for Predicting the Occurrence of Nephropathy in Diabetic Patients

by Benjamin Lartey, Kelvin Adrah, Frederick Adrah, Joan Isichei
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 24
Year of Publication: 2023
Authors: Benjamin Lartey, Kelvin Adrah, Frederick Adrah, Joan Isichei

Benjamin Lartey, Kelvin Adrah, Frederick Adrah, Joan Isichei . Application of Machine Learning for Predicting the Occurrence of Nephropathy in Diabetic Patients. International Journal of Computer Applications. 185, 24 ( Jul 2023), 38-43. DOI=10.5120/ijca2023923002

@article{ 10.5120/ijca2023923002,
author = { Benjamin Lartey, Kelvin Adrah, Frederick Adrah, Joan Isichei },
title = { Application of Machine Learning for Predicting the Occurrence of Nephropathy in Diabetic Patients },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 24 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2023923002 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:27:32.337956+05:30
%A Benjamin Lartey
%A Kelvin Adrah
%A Frederick Adrah
%A Joan Isichei
%T Application of Machine Learning for Predicting the Occurrence of Nephropathy in Diabetic Patients
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 24
%P 38-43
%D 2023
%I Foundation of Computer Science (FCS), NY, USA

This paper presents an in-depth technical analysis and comparison of various machine learning models for predicting the occurrence of nephropathy in diabetic patients. The models evaluated in this study encompass a wide range of algorithms, including logistic regression, support vector machines, decision trees, random forest, naive Bayes, k-nearest neighbors, gradient boosting machines, and fully connected neural network. The performance of these models is evaluated using accuracy, precision, and recall metrics. The findings from this extensive evaluation provide valuable insights into the strengths and limitations of each model, facilitating informed decision-making for selecting the most appropriate algorithm for predicting the occurrence of nephropathy in diabetic patients. The experimental results indicated that random forest exhibited an excellent performance whereas naive bayes algorithm performed poorly.

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Index Terms

Computer Science
Information Sciences


Nephropathy diabetic patients machine learning