Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Review Article

Machine Learning-Based Classification Models for Diagnosis of Diabetes

Author(s): Sushma Jaiswal and Tarun Jaiswal*

Volume 15, Issue 6, 2022

Published on: 01 February, 2021

Article ID: e090522190936 Pages: 9

DOI: 10.2174/2666255814666210201103252

Price: $65

Abstract

Introduction: The goal of this study is to expand the diabetes decision-making framework through the advancement of computational intelligence. Several artificial network and machine- learning-based methods have been developed and validated, most of which are based on the Pima Indian dataset. So far, no method has reached an accuracy of 99-100%. Various tools such as Machine Learning (ML) and Data Mining are used for the correct identification of diabetes. These tools improve the diagnostic process associated with T2DM. Diabetes mellitus type 2 (DMT2) is a major problem in several developing countries, and its early diagnosis can save several people’s lives. Accordingly, we have to build a structure that diagnoses type 2 diabetes. This paper proposes a fuzzy expert system that uses the Mamdani fuzzy inference structure (MFIS) to diagnose type 2 diabetes accurately. The proposed research work has been created using a variety of machine learning algorithms such as J48 Decision-tree (DT), Multilayer perceptron (MLP), Support-vector-machine (SVM), Naive-Bayes (NB), Fusion, and Mixed fusion-based. Actual data from the UCI machine learning datasets are used to validate the advanced Fuzzy expert system (FES) and machine learning algorithms.

Objective: A review of recent advances in machine learning-based classification models for diabetes diagnosis is presented in this survey paper.

Methods: This paper compares modified fusion processes to fundamental models such as radial basis function, K-nearest neighbor, support vector machine, J48, logistic regression, classification, regression trees, etc., for diagnosing type 2 diabetes.

Results: Figs. 3 and 4 show the results for each classifier based on prediction accuracy.

Conclusion: The fuzzy expert system is the best among its rival classifiers. SVM performs very poorly with a very low true positive rate, i.e., a very high number of positive cases misclassified as (non-diabetic) negative. Based on the evaluation, it is clear that the fuzzy expert system has the highest precision value. However, J48 is the least accurate classifier. Compared to the other classifiers listed in the testing section, it has the greatest number of false positives. The results show that the fuzzy expert system has the uppermost cost for both precision and recall. Thus, it has the uppermost value for F-measure in the training and testing datasets. J48 is considered the secondbest classifier for the training dataset, whereas Naïve Bayes comes in the second rank in the testing dataset.

Keywords: Diabetes judgement, diabetes mellitus, computational–procedure, machine learning, decision-tree, J48, SVM, MLP, NB, FES, MFIS.

Graphical Abstract

[1]
J.R. Gavin III, "New classification and diagnostic criteria for diabetes mellitus", Clin. Cornerstone, vol. 1, no. 3, pp. 1-12, 1998.
[http://dx.doi.org/10.1016/S1098-3597(98)90014-X] [PMID: 10682169]
[2]
L.J. Gray, and K. Khunti, "Type 2 diabetes risk prediction--do biomarkers increase detection?", Diabetes Res. Clin. Pract., vol. 101, no. 3, pp. 245-247, 2013.
[http://dx.doi.org/10.1016/j.diabres.2013.07.008] [PMID: 23928565]
[3]
K. Kayaer, "and T. Yıldırım, "Medical diagnosis on Pima Indian diabetes using general regression neural networks", Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP). pp. 181-184 2003
[4]
D.J. Magliano, A. Peeters, T. Vos, R. Sicree, J. Shaw, C. Sindall, M. Haby, S.J. Begg, and P.Z. Zimmet, "Projecting the burden of diabetes in Australia-what is the size of the matter?", Aust. N. Z. J. Public Health, vol. 33, no. 6, pp. 540-543, 2009.
[http://dx.doi.org/10.1111/j.1753-6405.2009.00450.x] [PMID: 20078571]
[5]
Machine Learning Repository U.C.I., Pima Indians Diabetes Data Set, 2016.https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
[6]
M. Kalpana, and A. Kumar, "Fuzzy Expert System for Diagnosis of Diabetes Using Fuzzy Determination Mechanism", International Journal of Advanced Research in Computer Science, vol. 3, no. 1, pp. 244-250, 2012.
[7]
I. Witten, and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques., 3rd ed Morgan Kaufmann: Amsterdam, London, 2011.
[8]
R. Gutierrez, "L18: Multi-Layer Perceptrons", CSCE 666 Pattern Analysis,. 2013
[9]
M. Kantardzic, Data mining: Concepts, models, methods, and algorithms., Wiley Interscience: USA, 2003.
[10]
J.C. Platt, "Fast training of support vector machines using sequential minimal optimization", Advances of kernel methods,, pp. 185-208, 1999.Cambridge,MA, USA,,
[11]
G. Flake, and S. Lawrence, "Efficient SVM Regression Training with SMO", Mach. Learn., vol. 46, pp. 271-290, 2002.
[http://dx.doi.org/10.1023/A:1012474916001]
[12]
I. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques., 2nd ed Elsevier: Amsterdam, London, 2005.
[13]
D. Almadni, and A. Abhari, "Comparative analysis of classification models in diagnosis of type 2 diabetes", the proceedings of Modeling and Simulation in Medicine (MSM) Symposium. 2016pp. 772- 776.
[14]
A. Adeli, and M. Neshat, "A Fuzzy Expert System for Heart Disease Diagnosis", The Proceedings of International Multi Conference of Engineers and Computer Scientist, 2010.
[15]
R. Parvin, and A. Abhari, "Fuzzy database for heart disease diagnosis", Proceedings of Medical Processes Modeling and Simulation (MPMS) Autumn Simulation Multi-Conference (SCS/AutumnSim’12). 2012
[16]
R. Pradhan, M. Aggarwal, D. Maheshwari, A. Chaturvedi, and D.K. Sharma, "Diabetes Mellitus Prediction and Classifier Comparative Study", International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), p. 2020pp. 133-139, .
[17]
F.M. Puchulu, Diagnosis and Classification of Diabetes Mellitus., Definition, 2018.
[18]
M.N. Sohail, R. Jiadong, M.M. Uba, M. Irshad, W. Iqbal, J. Arshad, and A.V. John, "A hybrid Forecast Cost Benefit Classification of dia-betes mellitus prevalence based on epidemiological study on Real-life patient’s data", Sci. Rep., vol. 9, no. 1, p. 10103, 2019.
[http://dx.doi.org/10.1038/s41598-019-46631-9] [PMID: 31300715]
[19]
D. Ente, and S. Arifin, Comparison of C4.5 algorithm with naive Bayesian method in classification of Diabetes Mellitus (A case study at Hasanuddin University hospital Makassar). 2019
[20]
M. Sharma, I. Petersen, I. Nazareth, and S.J. Coton, "An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database", Clin. Epidemiol., vol. 8, pp. 373-380, 2016.
[http://dx.doi.org/10.2147/CLEP.S113415] [PMID: 27785102]

Rights & Permissions Print Cite
Article Metrics
39
2
© 2024 Bentham Science Publishers | Privacy Policy