International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Classification of Machine Learning Techniques for Diabetic Diseases Prediction

  • Received : 2023.12.05
  • Published : 2023.12.30
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Abstract

Diabetes is a condition that can be brought on by a variety of different factors, some of which include, but are not limited to, the following: age, a lack of physical activity, a sedentary lifestyle, a family history of diabetes, high blood pressure, depression and stress, inappropriate eating habits, and so on. Diabetes is a disorder that can be brought on by a number of different factors. A chronic disorder that may lead to a wide range of complications. Diabetes mellitus is synonymous with diabetes. There is a correlation between diabetes and an increased chance of having a variety of various ailments, some of which include, but are not limited to, cardiovascular disease, nerve damage, and eye difficulties. There are a number of illnesses that are connected to kidney dysfunction, including stroke. According to the figures provided by the International Diabetes Federation, there are more than 382 million people all over the world who are afflicted with diabetes. This number will have risen during the years in order to reach 592 million by the year 2035. There are a substantial number of people who become victims on a regular basis, and a significant percentage of those people are uninformed of whether or not they have it. The individuals who are most adversely impacted by it are those who are between the ages of 25 and 74 years old. This paper reviews about various machine learning techniques used to detect diabetes mellitus.

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References

  1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2006, 29, S43.
  2. Jamison, D.T.; Breman, J.G.; Measham, A.R.; Alleyne, G.; Claeson, M.; Evans, D.B.; Jha, P.; Mills, A.; Musgrove, P. Disease Control Priorities in Developing Countries; World Bank Publications: Washington, DC, USA, 2006.
  3. World Health Organization (WHO). Diabetes Country Profiles 2016. 2016. Available online: https://cdn.who.int/media/docs/default-source/ncds/ncdsurveillance/diabetes_profiles_explanatory_notes.pdf?sfvrsn=f2a2083c_5&download=true (accessed on 1 July 2022). 
  4. Rewers, M.; Hamman, R.F. Risk factors for non-insulin-dependent diabetes. Diabetes Am. 1995, 2, 179-220.
  5. International Diabetes Federation (IDF). IDF Diabetes Atlas, 9th ed.; International Diabetes Federation: Brussels, Belgium, 2019.
  6. Guariguata, L.; Whiting, D.R.; Hambleton, I.; Beagley, J.; Linnenkamp, U.; Shaw, J.E. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract. 2014, 103, 137-149. https://doi.org/10.1016/j.diabres.2013.11.002
  7. NCD Risk Factor Collaboration (NCD-RisC); Walton, J. Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 2016, 387, 1513-1530. https://doi.org/10.1016/S0140-6736(16)00618-8
  8. Birjais R, Mourya AK, Chauhan R, Kaur H. Prediction and diagnosis of future diabetes risk:A machine learning approach. SN Appl Sci. 2019;1:1-8. https://doi.org/10.1007/s42452-019-1117-9
  9. Sadhu A, Jadli A. Early-stage diabetes risk prediction:A comparative analysis of classification algorithms. IntAdv Res J SciEngTechnol (IARJSET) 2021;8:193-201.
  10. Xue J, Min F, Ma F. Research on diabetes prediction method based on machine learning. J PhysConf Ser. 2020;1684:1-6. https://doi.org/10.1088/1742-6596/1684/1/012062
  11. Le TM, Vo TM, Pham TN, Dao SV. A novel wrapper-based feature selection for early diabetes prediction enhanced with a metaheuristic. IEEE Access. 2020;9:7869-84.
  12. Julius AO, Ayokunle AO, Ibrahim FO. Early diabetic risk prediction using machine learning classification techniques. Available from:https://ijisrt.com/early-diabetic-risk-prediction-using-machine-learning-classification-techniques .
  13. Shafi S, Ansari GA. Early prediction of diabetes disease &classification of algorithms using machine learning approach. In Proceedings of the International Conference on Smart Data Intelligence (ICSMDI 2021) Available from:SSRN 3852590 (2021)
  14. Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7:432-9. https://doi.org/10.1016/j.icte.2021.02.004
  15. Sisodia D, Sisodia DS. Prediction of diabetes using classification algorithms. Procedia Comput Sci. 2018;132:1578-85. https://doi.org/10.1016/j.procs.2018.05.122
  16. Agrawal P, Dewangan AK. A brief survey on the techniques used for the diagnosis of diabetes-mellitus. Int Res J Eng Tech IRJET. 2015;2:1039-43.
  17. Rathore A, Chauhan S, Gujral S. Detecting and predicting diabetes using supervised learning:An approach towards better healthcare for women. Int J Adv Res Comput Sci. 2017;8:1192-4.
  18. Hassan AS, Malaserene I, Leema AA. Diabetes mellitus prediction using classification techniques. Int J InnovTechnolExplor Eng. 2020;9:2080-4. https://doi.org/10.35940/ijitee.E2692.039520
  19. Kandhasamy JP, Balamurali S. Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput Sci. 2015;47:45-51. https://doi.org/10.1016/j.procs.2015.03.182
  20. Meng XH, Huang YX, Rao DP, Zhang Q, Liu Q. Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung J Med Sci. 2013;29:93-9. https://doi.org/10.1016/j.kjms.2012.08.016
  21. Nai-Arun N, Moungmai R. Comparison of classifiers for the risk of diabetes prediction. Procedia Comput Sci. 2015;69:132-42. https://doi.org/10.1016/j.procs.2015.10.014
  22. Saravananathan K, Velmurugan T. Analyzing diabetic data using classification algorithms in data mining. Indian J Sci Technol. 2016;9:1-6. https://doi.org/10.17485/ijst/2016/v9i43/93874
  23. 23. Kumari VA, Chitra R. Classification of diabetes disease using support vector machine. Int J Eng Res Appl. 2013;3:1797-801.
  24. 24. Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. ComputStructBiotechnol J. 2017;15:104-16. https://doi.org/10.1016/j.csbj.2016.12.005
  25. Rawat V, Suryakant S. A classification system for diabetic patients with machine learning techniques. Int J Math EngManag Sci. 2019;4:729-44. https://doi.org/10.33889/IJMEMS.2019.4.3-057
  26. Perveen S, Shahbaz M, Guergachi A, Keshavjee K. Performance analysis of data mining classification techniques to predict diabetes. Procedia Comput Sci. 2016;82:115-21. https://doi.org/10.1016/j.procs.2016.04.016
  27. Mujumdar A, Vaidehi V. Diabetes prediction using machine learning algorithms. Procedia Comput Sci. 2019;165:292-9. https://doi.org/10.1016/j.procs.2020.01.047
  28. Diabetes mellitus affected patients classification and diagnosis through machine learning techniques. Procedia Comput Sci. 2017;112:2519-28. https://doi.org/10.1016/j.procs.2017.08.193
  29. S. Wadhwa and K. Babber, "Artificial intelligence in health care: predictive analysis on diabetes using machine learning algorithms," in Proceeding of the International Conference on Computational Science and Its Applications, pp. 354-366, Springer, Cagliari, Italy, July 2020. 
  30. S. Majumder, Y. Elloumi, M. Akil, R. Kachouri, and N. Kehtarnavaz, "A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy," in Proceedings of the Real-Time Image Processing and Deep Learning 2020, vol. 11401, p. 1140106, International Society for Optics and Photonics, April 2020. 
  31. A. Hussain and S. Naaz, "Prediction of diabetes mellitus: comparative study of various machine learning models," in Proceeding of the International Conference on Innovative Computing and Communications, pp. 103-115, Springer, Delhi, India, January 2021. 
  32. G. Acciaroli, M. Vettoretti, A. Facchinetti, and G. Sparacino, "Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives," Biosensors, vol. 8, no. 1, 2018. 
  33. Zolfaghari R. Diagnosis of diabetes in female population of pima indian heritage with ensemble of bp neural network and svm. Int. J. Comput. Eng. Manag/ 2012;15:2230-7893.
  34. Sneha N., Gangil T. Analysis of diabetes mellitus for early prediction using optimal features selection. J. Big Data. 2019;6:13. doi: 10.1186/s40537-019-0175-6.
  35. Edeh M.O., Khalaf O.I., Tavera C.A., Tayeb S., Ghouali S., Abdulsahib G.M., Richard-Nnabu N.E., Louni A. A Classification Algorithm-Based Hybrid Diabetes Prediction Model. Front. Public Health. 2022;10:829519. doi: 10.3389/fpubh.2022.829519.
  36. Massaro A., Maritati V., Giannone D., Convertini D., Galiano A. LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction. Appl. Sci. 2019;9:3532. doi: 10.3390/app9173532.
  37. Dadgar S.M.H., Kaardaan M. A Hybrid Method of Feature Selection and Neural Network with Genetic Algorithm to Predict Diabetes. Int. J. Mechatron. Electr. Comput. Technol. (IJMEC) 2017;7:3397-3404.
  38. Zou Q., Qu K., Luo Y., Yin D., Ju Y., Tang H. Predicting Diabetes Mellitus With Machine Learning Techniques. Front. Genet. 2018;9:515. doi: 10.3389/fgene.2018.00515.
  39. Ashiquzzaman A., Tushar A.K., Islam M., Shon D., Im K., Park J.-H., Lim D.-S., Kim J. IT Convergence and Security 2017. Springer; Singapore: 2018. Reduction of overfitting in diabetes prediction using deep learning neural network; pp. 35-43.
  40. Kannadasan K., Edla D.R., Kuppili V. Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clin. Epidemiol. Glob. Health. 2019;7:530-535. doi: 10.1016/j.cegh.2018.12.004.
  41. Rahman M., Islam D., Mukti R.J., Saha I. A deep learning approach based on convolutional LSTM for detecting diabetes. Comput. Biol. Chem. 2020;88:107329. doi: 10.1016/j.compbiolchem.2020.107329.
  42. Alex S.A., Nayahi J., Shine H., Gopirekha V. Deep convolutional neural network for diabetes mellitus prediction. Neural Comput. Appl. 2022;34:1319-1327. doi: 10.1007/s00521-021-06431-7.
  43. Kalagotla S.K., Gangashetty S.V., Giridhar K. A novel stacking technique for prediction of diabetes. Comput. Biol. Med. 2021;135:104554. doi: 10.1016/j.compbiomed.2021.104554.
  44. Jakka A., Vakula Rani J. Performance evaluation of machine learning models for diabetes prediction. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2019;8:1976-1980. https://doi.org/10.35940/ijitee.K2155.0981119
  45. Sheetal, Dr Sukhvinder Singh Deora, "DIABETIC DISEASES PREDICTION USING MACHINE LEARNING TECHNIQUES: A REVIEW" in Proceedings of the National Conference on Computational Intelligence and Data Science (NCCIDS-23), March, 2023, MDU, Rohtak, pp. 195-199. 
  46. Sukhvinder Singh Deora, Mandeep Kaur, "Image Processing and Computer Vision: Relevance and Applications in the Modern World" in Nova Science Publishers, 2023, The Impact of Thrust Technologies on Image Processing, https://doi.org/10.52305/ATJL4552 Volume 1 Pages 233-252