Machine Learning For Non- Invasive Diagnostics Of Glucose Metabolism Disorder

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Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.968
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Volume 13, Special Issue 5, November 2022

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Suruchi Dive
G.H.RAISONI UNIVERSITY,SAIKHEDA,MADHYA PRADESH
Dr. Gopal Sakarkar
G.H.RAISONI UNIVERSITY,SAIKHEDA,MADHYA PRADESH, INDIA
https://orcid.org/0000-0002-2108-4827

Abstract

Glucose metabolism disorder known as Diabetes Mellitus is a state created by uncontrolled blood sugar that may lead to serious damage to multiple organs in patients. Identifying and predicting this disease will save human life. While designing medical diagnosis software, disease prediction is said to be one of the capricious tasks. In the current scenario, many researchers have provided their ideas on using machine learning and artificial intelligence for automated prediction of Diabetes Mellitus. A set of five popular Naïve Bayes, Random Forest, SVM, KNN and Decision Tree have been identified as well as a set of four rarely used GPC, QDA, LDA and AdaBoost have been identified from literature survey. The study is an effort to make a comparative report of the accuracy of two sets and identify the best performer. In conclusion, Support Vector Machine achieved highest accuracy with 81.00% in popular classifiers whereas Linear Discriminant Analysis achieved highest accuracy with 82.00% in less frequently used classifiers. Hence, more such rarely used classifiers should be explored for the realistic health management of diabetes.


 

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Suruchi Dive, G.H.RAISONI UNIVERSITY,SAIKHEDA,MADHYA PRADESH

Suruchi Dive (Pimple) is a Research Scholar at School of Science , G.H.Raisoni University, Saikheda, Madhya Pradesh, India. She is working as an Assistant Professor at Sadabai Raisoni Women's College, Nagpur, Maharashtra. She has an experience in teaching of 18 years. Her areas of interest are machine learning and biomedical sciences. She is a life member of the CSI.

Dr. Gopal Sakarkar, G.H.RAISONI UNIVERSITY,SAIKHEDA,MADHYA PRADESH, INDIA

Dr.Gopal Sakarkar is a Research Guide at G.H.Raisoni University, Saikheda, Madhya Pradesh,India. He has done his Ph.D. in Computer Science and Engineering. The author has contributed to research in topic(s): Software agent & Deep learning. He has an hindex of 4, co-authored 10 publication(s) receiving 101 citation(s).He was appointed Foreign Specialist in three-member panel for PhD thesis review at the University of Petrosani , Romania. His research interests are in Software agent, Deep learning, Autonomous agent, Intelligent agent, Valence (psychology).

How to Cite
Suruchi Dive, & Gopal Sakarkar. (2022). Machine Learning For Non- Invasive Diagnostics Of Glucose Metabolism Disorder. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.968
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References

  1. Han Wu, Shengqi Yang, Zhangqin Huang, Jian He, Xiaoyi Wang, Type 2 diabetes mellitus prediction model based on data mining, 2018,Informatics in Medicine Unlocked, Vol 10, Pg 100-107, doi.org/10.1016/j.imu.2017.12.006. DOI: https://doi.org/10.1016/j.imu.2017.12.006
  2. Sulaiman N, Mahmoud I, Hussein A, et al., Diabetes risk score in the United Arab Emirates: a screening tool for the early detection of type 2 diabetes mellitus,2018,BMJ Open Diab Res Care 2018;6:e000489. doi:10.1136/ bmjdrc-2017-000489 DOI: https://doi.org/10.1136/bmjdrc-2017-000489
  3. Gunčar, G., Kukar, M., Notar, M. et al. , An application of machine learning to haematological diagnosis, 2018, Sci Rep 8, 411, https://doi.org/10.1038/s41598-017-18564-8 DOI: https://doi.org/10.1038/s41598-017-18564-8
  4. Ambika Choudhury, Deepak Gupta, A Survey on Medical Diagnosis of Diabetes Using Machine Learning Techniques, 2019, Recent Developments in Machine Learning and Data Analytics, Volume 740 ISBN : 978-981-13-1279-3
  5. Raja Krishnamoorthi, Shubham Joshi, Hatim Z. Almarzouki, Piyush Kumar Shukla, Ali Rizwan, C. Kalpana, Basant Tiwari, "A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques", Journal of Healthcare Engineering, vol. 2022, Article ID 1684017, 10 pages, 2022 https://doi.org/10.1155/2022/1684017 DOI: https://doi.org/10.1155/2022/1684017
  6. H. Abbas, L. Alic, M. Rios, M. Abdul-Ghani and K. Qaraqe, Predicting Diabetes in Healthy Population through Machine Learning, 2019, IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 2019, pp. 567-570, doi: 10.1109/CBMS.2019.00117. DOI: https://doi.org/10.1109/CBMS.2019.00117
  7. R. Aminah and A. H. Saputro, "Diabetes Prediction System Based on Iridology Using Machine Learning," 2019, 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), Semarang, Indonesia, 2019, pp. 1-6, doi: 10.1109/ICITACEE.2019.8904125. DOI: https://doi.org/10.1109/ICITACEE.2019.8904125
  8. D. Vigneswari, N. K. Kumar, V. Ganesh Raj, A. Gugan and S. R. Vikash, Machine Learning Tree Classifiers in Predicting Diabetes Mellitus, 2019 ,5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 84- 87, doi: 10.1109/ICACCS.2019.8728388 DOI: https://doi.org/10.1109/ICACCS.2019.8728388
  9. Nur Hadirah Khairul Anwar, Rizauddin Saian, Predictive Accuracy For Two Diabetes Datasets Using Ant-Miner Algorithm,2020, International Journal Of Scientific & Technology Research Volume 9, Issue 04
  10. M. K. Hasan, M. A. Alam, D. Das, E. Hossain and M. Hasan, "Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers," in IEEE Access, vol. 8, pp. 76516- 76531, 2020, doi: 10.1109/ACCESS.2020.2989857. DOI: https://doi.org/10.1109/ACCESS.2020.2989857
  11. L. Miao, X. Guo, H. T. Abbas, K. A. Qaraqe and Q. H. Abbasi, Using Machine Learning to Predict the Future Development of Disease, 2020, International Conference on UK-China Emerging Technologies (UCET), Glasgow, UK, 2020, pp. 1-4, doi: 10.1109/UCET51115.2020.9205373 DOI: https://doi.org/10.1109/UCET51115.2020.9205373
  12. Lai, H., Huang, H., Keshavjee, K. et al. Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr Disord 19, 101 (2019). https://doi.org/10.1186/s12902-019-0436-6 DOI: https://doi.org/10.1186/s12902-019-0436-6
  13. Lv Wenqi,Fu, Rongxin, Lin, Xue,Su, Ya,Jin, Xiangyu, Yang, Han,Shan, Xiaohui,Du, Wenli,Jiang, Kai,Lin, Yuanhua, Huang, Guoliang, A non-invasive diabetes diagnosis method based on novel scleral imaging instrument and AI, Proceedings Volume 11900, Optics in Health Care and Biomedical Optics XI; 1190013 (2021) https://doi.org/10.1117/12.2601222 DOI: https://doi.org/10.1117/12.2601222
  14. Maniruzzaman, M., Rahman, M.J., Al-MehediHasan, M. et al. Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers,2018, J Med Syst 42, 92, https://doi.org/10.1007/s10916-018-0940-7 DOI: https://doi.org/10.1007/s10916-018-0940-7
  15. M. F. Faruque, Asaduzzaman and I. H. Sarker, Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus, 2019, International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox's Bazar, Bangladesh, pp. 1-4, doi: 10.1109/ECACE.2019.8679365. DOI: https://doi.org/10.1109/ECACE.2019.8679365
  16. Naz, H., Ahuja, S. Deep learning approach for diabetes prediction using PIMA Indian dataset, 2020, J Diabetes Metab Disord 19, 391–403, https://doi.org/10.1007/s40200-020- 00520-5 DOI: https://doi.org/10.1007/s40200-020-00520-5
  17. Nguyen BP, Pham HN, Tran H, Nghiem N, Nguyen QH, Do TTT, Tran CT, Simpson CR. Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Comput Methods Programs Biomed. 2019 Dec;182:105055. doi: 10.1016/j.cmpb.2019.105055. Epub 2019 Aug 27. PMID: 31505379. DOI: https://doi.org/10.1016/j.cmpb.2019.105055
  18. Safial Islam Ayon, Md. Milon Islam , Diabetes Prediction: A Deep Learning Approach, 2019, I.J. Information Engineering and Electronic Business, 2, 21-27 Published Online March 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijieeb.2019.02.03 Copy- right © 2019 DOI: https://doi.org/10.5815/ijieeb.2019.02.03
  19. Deepti Sisodia, Dilip Singh Sisodia,Prediction of Diabetes using Classification Algorithms,2018, Science Direct, Procedia Computer Science,Volume 132,Pg.1578-1585, doi.org/10.1016/j.procs.2018.05.122 DOI: https://doi.org/10.1016/j.procs.2018.05.122
  20. N.Sneha, Tarun Gangil , Analysis of Diabetes Analysis of diabetes mellitus for early prediction using optimal features selection, 2019,Journal of Big data, 6:13 https://doi.org/10.1186/s40537-019-0175-6 DOI: https://doi.org/10.1186/s40537-019-0175-6
  21. Srivastava S., Sharma L., Sharma V., Kumar A., Darbari H. Prediction of Diabetes Using Artificial Neural Network Approach, 2019, In: Ray K., Sharan S., Rawat S., Jain S., Srivastava S., Bandyopadhyay A. (eds) Engineering Vibration, Communication and Information Pro- cessing. Lecture Notes in Electrical Engineering, vol 478. Springer, Singapore. https://doi.org/10.1007/978-981-13-1642-5_59 DOI: https://doi.org/10.1007/978-981-13-1642-5_59
  22. Xie Z, Nikolayeva O, Luo J, Li D. Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques,2019, Prev Chronic Dis. 2019 Sep 19;16:E130, doi: 10.5888/pcd16.190109. PMID: 31538566; PMCID: PMC6795062. DOI: https://doi.org/10.5888/pcd16.190109
  23. Xiong, Xi., Zhang, Rx., Bi, Y. et al. Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults,2019,CURR MED SCI 39, 582–588, https://doi.org/10.1007/s11596-019-2077-4 DOI: https://doi.org/10.1007/s11596-019-2077-4