A Review of Diverse Diabetic Prediction Models: A Literature Study
DOI:
https://doi.org/10.38043/tiers.v4i2.3617Keywords:
Machine learning, Data Mining, Random Forest, Diabetes, Voting Classifier, Naïve Bayes, SVM, AdaBoost Decision TreeAbstract
Diabetes is a disease described by extreme glucose measurement in the blood and can trigger an excessive number of problems likewise in the body, like the failure of internal organs, retinopathy, and neuropathy. As per the forecasts made by World Health Organization, the figure might reach roughly 642 million by 2040, and that implies one in ten might experience diabetic diseases due to various reasons such as low activity levels, unhealthy routines, and schedules, rising tension levels and so on. Many researchers in the past have explored widely on diabetes disease through AI calculations and ML algorithms. The possibility that had persuaded us to introduce a survey of different prediction models of diabetic disease is to address the diabetes issue by recognizing and coordinating the discoveries of all-important, individual examinations. In this research, we have analyzed the different prediction algorithms and techniques by different researchers that how they predict diabetic disease. Also, we have analyzed the PIMA and symptom and other datasets and how they reach their resultant accuracy by applying different classifiers. Because of non-linear, correlated, and complex structured data in the medical field, diabetic data analysis is very difficult. That’s why Ml-based algorithms have been utilized for the prediction of diabetic disease and handle a large amount of data and it needs a different approach from others at the initial stage. We emphatically suggest our review since it involves articles from different sources that will assist different specialists with different models of prediction for diabetes.
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References
Adel Hussien; Safaa saleh; Hyam R. Tantawi, “ Mothers ' knowledge and Practices toward Their Children Suffering from Juvenile Diabetes: an Assessment Study”, Egyptian Journal of healthcare, Volume 10, Issue 2, 2019, https://doi: 10.21608/EJHC.2019.36696
S. Malik, S. Harous, and H. El-Sayed, “Comparative analysis of machine learning algorithms for early prediction of diabetes mellitus in women,” in Proceedings of the International Symposium on Modelling and Implementation of Complex Systems, pp. 95–106, Springer, Algeria, October 2020.
J. Han, J. C. Rodriguez, and M. Behesti, “Discovering Decision Tree-Based Diabetes Prediction Model,” in Proceedings of the International Conference on Advanced Software Engineering and its Applications, pp. 99–109, Springer, Jeju Island, Korea, December 2018.
Mahesh, Batta. (2019). Machine Learning Algorithms -A Review. https://doi:10.21275/ART20203995.
X. Xu, X. Huang, J. Ma and X. Luo, “Prediction of Diabetes with its Symptoms Based onMachine Learning,” 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE), 2021, pp.147-156, doi:10.1109/CSAIEE54046.2021.9543343.
Iopscience.iop.org. 2022. ShieldSquare Captcha. [online] Available at: <https://iopscience.iop.org/article/10.1088/1742- 6596/1684/1/012062/pdf> [Accessed 8 June 2022].
M. U. Emon, M. S. Keya, M. S. Kaiser, M.A. islam, T. Tanha and M. S. Zulfiker, “Primary Stage of Diabetes Prediction using Machine Learning Approaches,” 2021 International Conferenceon Artificial Intelligenceand Smart Systems (ICAIS), 2021, pp.364-367, doi:10.1109/ICAIS50930.2021.9395968.
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 AdvancedComputing & Communication Systems (ICACCS), 2019, pp. 84-87, doi: 10.1109/ICACCS.2019.8728388.
A. Yahyaoui, A. Jamil, J. Rasheed and M. Yesiltepe, “A Decision Support System for diabetesPrediction Using Machine
Learning and Deep Learning Techniques,” 2019 1stInternational Informatics and Software Engineering Conference (UBMYK), 2019, pp. 1-4,doi:10.1109/UBMYK48245.2019.8965556.
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.
D. Dutta, D. Paul and P. Ghosh, “Analysing Feature Importances for Diabetes Prediction usingMachine Learning,” 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2018, pp. 924-928, doi: 10.1109/IEMCON.2018.8614871.
Tigga, Neha Prerna, and Shruti Garg. “Prediction of Type 2 Diabetes Using Machine LearningClassification Methods.” Procedia Computer Science, vol. 167, 2020, pp.706–716., https://doi.org/10.101 6/j.procs.2020.03.336.
P. Sonar and K. JayaMalini, “Diabetes Prediction Using Different Machine Learning Approaches,” 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 2019, pp.367-371, doi:10.1109/ICCMC.2019.8819841.
Aishwarya Mujumdar, V Vaidehi, Diabetes Prediction using Machine Learning Algorithms,Procedia Computer Science, Volume 165, 2019, Pages 292-299, ISSN 1877-0509, https://doi.org/10.101 6/j.procs.2020.01.047.
Naz, H., Ahuja, S. Deep learning approach for diabetes prediction using PIMA Indian dataset.J Diabetes Metab Disord 19, 391–403 (2020). https://doi.org/10.1007/s40200-020- 00520-5.
Zaigham Mushtaq, Muhammad Farhan Ramzan, Sikandar Ali, Samad Baseer, Ali Samad, Mujtaba Husnain, “Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques”, Mobile Information Systems, vol. 2022, Article ID 6521532, 16pages, 2022. https://doi.org/10.1155/2022/6521532
S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and predictionof diabetes mellitus using soft voting classifier,” International Journal of Cognitive Computing inEngineering, vol. 2, 2021.
S. Malik, S. Harous, and H. E. Sayed, “Comparative analysis of machine learning algorithmsforearly predictionof diabetes mellitus in women,” in Proceedingsofthe International Symposiumon Modelling and Implementation of Complex Systems,
pp. 95–106, Springer, Batna, Algeria, October 2020.
A. Yahyaoui, A. Jamil, J. Rasheed and M. Yesiltepe, “A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques,” 2019 1st International Informatics and Software Engineering Conference (UBMYK), 2019, pp. 1-4, doi: 10.1109/UBMYK48245.2019.8965556.
Deepti Sisodia, Dilip Singh Sisodia, Prediction of Diabetes using Classification Algorithms, Procedia Computer Science, Volume 132, 2018, Pages 1578-1585, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2018.05.122.
Birjais, R., Mourya, A.K., Chauhan, R. et al. Prediction and diagnosis of future diabetes risk: a machine learning approach. SN Appl. Sci. 1, 1112 (2019). https://doi.org/10.1007/s42452-019-1117-9
U. Ahmed et al., "Prediction of Diabetes Empowered With Fused Machine Learning," in IEEE Access, vol. 10, pp. 8529- 8538, 2022, doi: 10.1109/ACCESS.2022.3142097.
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). https://doi.org/10.1007/s13755-019-0095-z
S. Prasanth, M. Roshni Thanka, E. Bijolin Edwin, V. Ebenezer. (2021). Prognostication of Diabetes Diagnosis Based on Different Machine Learning Classification Algorithms. Annals of the Romanian Society for Cell Biology, 372–395.
Retrieved from https://www.annalsofrscb.ro/index.php/journal/article/view/4299
Benbelkacem and B. Atmani, "Random Forests for Diabetes Diagnosis," 2019 International Conference on Computer and Information Sciences (ICCIS), 2019, pp. 1-4, doi: 10.1109/ICCISci.2019.8716405.
S. Islam Ayon, M. Milon Islam, and M. Milon Islam, “Diabetes prediction: a deep learning approach,” International Journal of Information Engineering and Electronic Business, vol. 11, no. 2, pp. 21–27, 2019.
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. and Tang, H., 2022. Predicting Diabetes Mellitus with Machine Learning Techniques.
Kaur, H. and Kumari, V. (2022), "Predictive modelling and analytics for diabetes using a machine learning approach", Applied Computing and Informatics, Vol. 18 No. 1/2, pp. 90-100. https://doi.org/10.1016/j.aci.2018.12.004
Umair Muneer Butt, Sukumar Letchmunan, Mubashir Ali, Fadratul Hafinaz Hassan, Anees Baqir, Hafiz Husnain Raza Sherazi, "Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications", Journal of Healthcare Engineering, vol. 2021, Article ID 9930985, 17 pages, 2021. https://doi.org/10.1155/2021/9930985
H. Song and S. Lee, "Implementation of Diabetes Incidence Prediction Using a Multilayer Perceptron Neural Network," 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 3089-3091, doi: 10.1109/BIBM52615.2021.9669583.
B. Paul and B. Karn, "Diabetes Mellitus Prediction using Hybrid Artificial Neural Network," 2021 IEEE Bombay Section Signature Conference (IBSSC), 2021, pp. 1-5, doi: 10.1109/IBSSC53889.2021.9673397.
Dergipark.org.tr. 2022. [online] Available at: <https://dergipark.org.tr/en/download/article-file/1648927> [Accessed 2
June 2022].
2022. [online] Available at: <https://www.ijert.org/research/diabetes-prediction-using-machine-learning-techniques-
IJERTV9IS090496.pdf> [Accessed 2 June 2022].
H. Alshamlan, H. B. Taleb and A. Al Sahow, "A Gene Prediction Function for Type 2 Diabetes Mellitus using Logistic Regression," 2020 11th International Conference on Information and Communication Systems (ICICS), 2020, pp. 1-4, doi: 10.1109/ICICS49469.2020.239549.
N. Dunbray, R. Rane, S. Nimje, J. Katade and S. Mavale, "A Novel Prediction Model for Diabetes Detection Using Gridsearch and A Voting Classifier between Lightgbm and KNN," 2021 2nd Global Conference for Advancement in Technology (GCAT), 2021, pp. 1-7, doi: 10.1109/GCAT52182.2021.9587551.
M. S. Diab, S. Husain and A. Jarndal, "On Diabetes Classification and Prediction using Artificial Neural Networks," 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), 2020, pp. 1-5, doi: 10.1109/CCCI49893.2020.9256621.
S. Yadav, Y. P. S. Maravi, J. Agrawal and N. Mishra, "A Neural Network based Diabetes Prediction on Imbalanced Data," 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 2021, pp. 515- 521, doi: 10.1109/CSNT51715.2021.9509732.
N. E. Costea, E. V. Moisi and D. E. Popescu, "Comparison of Machine Learning Algorithms for Prediction of Diabetes," 2021 16th International Conference on Engineering of Modern Electric Systems (EMES), 2021, pp. 1-4, doi: 10.1109/EMES52337.2021.9484116.
P. Saxena, S. Saha and S. K. Devi, "SA pp. 315-319, doi: 10.1109/MECON53876.2022.9751854.
P. Prabhu and S. Selvabharathi, "Deep Belief Neural Network Model for Prediction of Diabetes Mellitus," 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), 2019, pp. 138-142, doi: 10.1109/ICISPC.2019.8935838.
A. Agarwal and A. Saxena, "Analysis of Machine Learning Algorithms and Obtaining Highest Accuracy for Prediction of Diabetes in Women," 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), 2019, pp. 686-690.
S. A. Aboalnaser and H. R. Almohammadi, "Comprehensive Study of Diabetes Miletus Prediction Using Different Classification Algorithms," 2019 12th International Conference on Developments in eSystems Engineering (DeSE), 2019,
pp. 128-133, doi: 10.1109/DeSE.2019.00033.
Khanam, J. and Foo, S., 2021. A comparison of machine learning algorithms for diabetes prediction. ICT Express, 7(4), pp.432-439.
G. Battineni, G. G. Sagaro, C. Nalini, F. Amenta, and S. K. Tayebati, “Comparative machine-learning approach: a follow- up study on type 2 diabetes predictions by cross-validation methods,” Machines, vol. 7, no. 4, pp. 74–11, 2019.
Gupta, H., Varshney, H., Sharma, T.K. et al. Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00398-7
Mahboob Alam, T., Iqbal, M., Ali, Y., Wahab, A., Ijaz, S., Imtiaz Baig, T., Hussain, A., Malik, M., Raza, M., Ibrar, S. and Abbas, Z., 2022. A model for early prediction of diabetes.
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