Classification and prediction of diabetes disease using machine learning paradigm

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Background and objectives

Diabetes is a chronic disease characterized by high blood sugar. It may cause many complicated disease like stroke, kidney failure, heart attack, etc. About 422 million people were affected by diabetes disease in worldwide in 2014. The figure will be reached 642 million in 2040. The main objective of this study is to develop a machine learning (ML)-based system for predicting diabetic patients.

Materials and methods

Logistic regression (LR) is used to identify the risk factors for diabetes disease based on p value and odds ratio (OR). We have adopted four classifiers like naïve Bayes (NB), decision tree (DT), Adaboost (AB), and random forest (RF) to predict the diabetic patients. Three types of partition protocols (K2, K5, and K10) have also adopted and repeated these protocols into 20 trails. Performances of these classifiers are evaluated using accuracy (ACC) and area under the curve (AUC).


We have used diabetes dataset, conducted in 2009–2012, derived from the National Health and Nutrition Examination Survey. The dataset consists of 6561 respondents with 657 diabetic and 5904 controls. LR model demonstrates that 7 factors out of 14 as age, education, BMI, systolic BP, diastolic BP, direct cholesterol, and total cholesterol are the risk factors for diabetes. The overall ACC of ML-based system is 90.62%. The combination of LR-based feature selection and RF-based classifier gives 94.25% ACC and 0.95 AUC for K10 protocol.


The combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.

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The authors would like to acknowledge the contribution of Statistics Discipline, Science, Engineering and Technology School, Khulna University, Khulna-9208, Bangladesh. The authors also thank to the editor and reviewers for their comments and positive critique.


No fund received for this project.

Author information

Md. Maniruzzaman: Statistical analysis, draft the original manuscript, and principal investigator and management of the project. Md. Jahanur Rahman: Acquisition of data, interpretation of the results and methodology; Benojir Ahammed: Machine learning concepts and design. Md. Menhazul Abedin: Data preprocessing, English writing, strategy, and interpretation.

Correspondence to Md. Maniruzzaman.

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The authors declare that they have no conflict of interest.

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Appendix 1

See Table 9.

Table 9 Description of the diabetes database

Appendix 2

See Tables 10, 11, and 12.

Table 10 System accuracy of 4 classifiers varying data sizes for K2 protocol
Table 11 System accuracy of 4 classifiers varying data sizes for K5 protocol
Table 12 System accuracy of 4 classifiers varying data sizes for K10 protocol

Appendix 3

See Table 13.

Table 13 List of abbreviations

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Maniruzzaman, M., Rahman, M.J., Ahammed, B. et al. Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst 8, 7 (2020) doi:10.1007/s13755-019-0095-z

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  • Diabetes
  • Classification
  • Machine learning
  • Naïve Bayes
  • Decision tree
  • Random forest
  • Adaboost