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An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed

An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease

Author (s)

Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed

Abstract

In today’s era everybody is trying to be conscious about health. Although, due to workload and busy schedule, one gives attention to the health when any major symptoms occur. But Chronic Kidney Disease (CKD) is a disease which doesn’t shows symptoms it is hard to predict, detect and prevent such a disease and this can lead to permanently health damage, but some machine learning algorithms can come handy in this aspect for their efficient prediction and analysis. By using data of CKD, patients with 25 attributes and 400 records we are going to use various machine learning techniques like Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree etc. The purposes of our work is to virtuously predicting Chronic Kidney disease and have a comparative analysis among some of the popular machine learning based approaches based on some performance metrics. In our work, it is found that the Random Forest algorithm outperforming other machine learning based approaches we used in the experiment.

 Keywords: CKD, KNN, Machine Learning, Prediction, Performance Metrics.

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Title: An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease
Author: Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4244468
Media: Online
Volume: 4
Issue: 11
Acceptance Date: 27/10/2020
Date of Publication: 28/10/2020
PDF URL: https://ijsab.com/wp-content/uploads/615.pdf
Free download: Available
Page: 101-110
First Page: 101
Last Page: 110
Paper Type: Research article
Current Status: Published

 

Cite This Article:

Md. Shafiul Azam, Umme Kulsom, S. M. Hasan Sazzad Iqbal & Md. Toukir Ahmed (2020). An Empirical Study of Various Machine Learning Approaches in Prediction of Chronic Kidney Disease, International Journal of Science and Business, 4(11), 1-8. doi: https://doi.org/10.5281/zenodo.4244468

Retrieved from https://ijsab.com/wp-content/uploads/615.pdf

 

About Author (s)

Md. Shafiul Azam Assistant Professor,  Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.

Umme Kulsom, Student, Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

M. Hasan Sazzad Iqbal, Assistant Professor, Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Md. Toukir Ahmed (Corresponding Author), Lecturer,  Dept. of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.

 

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DOI: https://doi.org/10.5281/zenodo.4244468

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Deep Learning Models Based on Image Classification: A Review Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed

Deep Learning Models Based on Image Classification: A Review

Author (s)

Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed

Abstract

With the development of the big data age, deep learning developed to become having a more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The model trained by the deep learning algorithm has made remarkable achievements in many large-scale identification tasks in the field of computer vision since its introduction. This paper first introduces the deep learning, and then the latest model that has been used for image classification by deep learning are reviewed.  Finally, all used deep learning models in the literature have been compared to each other in terms of accuracy for the two most challenging datasets CIFAR-10 and CIFAR-100.

 Keywords: Deep Learning, Image Classification, Machine Learning, Models.

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Title: Deep Learning Models Based on Image Classification: A Review
Author: Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4108433
Media: Online
Volume: 4
Issue: 11
Acceptance Date: 13/10/2020
Date of Publication: 20/10/2020
PDF URL: https://ijsab.com/wp-content/uploads/612.pdf
Free download: Available
Page: 75-81
First Page: 75
Last Page: 81
Paper Type: Research article
Current Status: Published

 

Cite This Article:

Kavi B. Obaid, Subhi R. M. Zeebaree & Omar M. Ahmed (2020). Deep Learning Models Based on Image Classification: A Review. International Journal of Science and Business, 4(11), 75-81. doi: https://doi.org/10.5281/zenodo.4108433

Retrieved from https://ijsab.com/wp-content/uploads/612.pdf

 

About Author (s)

Kavi B. Obaid, Computer Science Department, College of Science, University of Zakho, Iraq (e-mail: kavi.obaid@uoz.edu.krd).

Subhi R. M. Zeebaree, Duhok Polytechnic University, Iraq (e-mail: subhi.rafeeq@dpu.edu.krd).

Omar M. Ahmed, (Corresponding author),Information Technology Department, Zakho Technical Institute, Duhok Polytechnic University, Iraq (e-mail: omar.alzakholi@uoz.edu.krd).

 

 

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DOI: https://doi.org/10.5281/zenodo.4108433