Prediction model for diabetes mellitus using machine learning algorithms for enhanced diagnosis and prognosis in healthcare
Vol 2, Issue 1, 2024
VIEWS - 4717 (Abstract)
Download PDF
Abstract
Diabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed, and human lives can be saved with early diagnosis. Smart prediction techniques like machine learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, along with five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC) and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring.
Keywords
References
1. Mahajan S, Sarangi PK, Sahoo AK, et al. Diabetes Mellitus Prediction using Supervised Machine Learning Techniques. In: Proceedings of the 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT); 5-6 May 2023; Gharuan, India. pp. 587-592. doi: 10.1109/incacct57535.2023.10141734
2. Charles RKJ, Mary AB, Jenova R, et al. VLSI design of intelligent, Self-monitored and managed, Strip-free, Non-invasive device for Diabetes mellitus patients to improve Glycemic control using IoT. Procedia Computer Science. 2019; 163: 117-124. doi: 10.1016/j.procs.2019.12.093
3. Balaji KV, Sugumar R. A Comprehensive Review of Diabetes Mellitus Exposure and Prediction using Deep Learning Techniques. In: Proceedings of the 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI); 8-10 December 2022; Chennai, India. pp. 1-6. doi: 10.1109/icdsaai55433.2022.10028832
4. Ansari RM, Harris MF, Hosseinzadeh H, et al. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare. 2023; 11(6): 903. doi: 10.3390/healthcare11060903
5. Chaki J, Thillai Ganesh S, Cidham SK, et al. Machine learning and artificial intelligence-based Diabetes Mellitus detection and self-management: A systematic review. Journal of King Saud University - Computer and Information Sciences. 2022; 34(6): 3204-3225. doi: 10.1016/j.jksuci.2020.06.013
6. Charles Rajesh Kumar J, Baskar D, Mary Arunsi B, Vinod Kumar D. Energy-Efficient and Secure IoT Architecture Based on a Wireless Sensor Network Using Machine Learning to Predict Mortality Risk of Patients with CoVID-19. In: Proceedings of the 2021 6th International Conference on Communication and Electronics Systems (ICCES); 8-10 July 2021; Coimbatore, India. pp. 1853-1861. doi: 10.1109/ICCES51350.2021.948895
7. Kumar JCR, Arunsi BM, Majid MA. A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations. In: Proceedings of the 2023 20th Learning and Technology Conference (L&T); 26 January 2023; Jeddah, Saudi Arabia. pp. 120-127. doi: 10.1109/lt58159.2023.10092331
8. Charles Rajesh Kumar J, Mary Arunsi B, Majid MA. Energy-Efficient IoT-Based Wireless Sensor Network Framework for Detecting Symptomatic and Asymptomatic COVID-19 Patients Using a Fuzzy Logic Approach. In: Karthick GS, Karupusamy S (editors). Contemporary Applications of Data Fusion for Advanced Healthcare Informatics. IGI Global; 2023. pp. 25-51. doi: 10.4018/978-1-6684-8913-0.ch002
9. Dayanand Lal N, Hema K, Spoorthi T, et al. An Effective Expectation of Diabetes Mellitus via Improved Support Vector Machine through Cloud Security. In: Proceedings of the 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS); 24-25 February 2023; Raichur, India. pp. 1-8. doi: 10.1109/icicacs57338.2023.10099888
10. Bharani R, Manishaa KT, Jaisurya D. Ram Kumar C. Diabetes Mellitus Diagnosis based on Tongue Images using Machine Learning. In: Proceedings of the 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS); 17-18 March 2023; Coimbatore, India. pp. 1614-1618. doi: 10.1109/icaccs57279.2023.10112849
11. Laxmikant K, Bhuvaneswari R, Natarajan B. An Efficient Approach to Detect Diabetes Using XGBoost Classifier. In: Proceedings of the 2023 Winter Summit on Smart Computing and Networks (WiSSCoN); 15-17 March 2023; Chennai, India. pp. 1-8. doi: 10.1109/wisscon56857.2023.10133854
12. Malik A, Parihar V, Srivastava J, et al. Prognosis of Diabetes Mellitus Based on Machine Learning Algorithms. In: Proceedings of the 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom); 15-17 March 2023; New Delhi, India. pp. 1466-1472.
13. Agliata A, Giordano D, Bardozzo F, et al. Machine Learning as a Support for the Diagnosis of Type 2 Diabetes. International Journal of Molecular Sciences. 2023; 24(7): 6775. doi: 10.3390/ijms24076775
14. Faruque MdF, Asaduzzaman, Sarker IH. Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus. In: Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE); 7-9 February 2019; Cox'sBazar, Bangladesh. pp. 1-4. doi: 10.1109/ecace.2019.8679365
15. Patil R, Tamane S. A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes. International Journal of Electrical and Computer Engineering (IJECE). 2018; 8(5): 3966. doi: 10.11591/ijece.v8i5.pp3966-3975
16. Iparraguirre-Villanueva O, Espinola-Linares K, Flores Castañeda RO, et al. Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes. Diagnostics. 2023; 13(14): 2383. doi: 10.3390/diagnostics13142383
17. Abegaz TM, Ahmed M, Sherbeny F, et al. Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data. Healthcare. 2023; 11(8): 1138. doi: 10.3390/healthcare11081138
18. Pranto B, Mehnaz SkM, Mahid EB, et al. Evaluating Machine Learning Methods for Predicting Diabetes among Female Patients in Bangladesh. Information. 2020; 11(8): 374. doi: 10.3390/info11080374
19. Syed AH, Khan T. Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study. IEEE Access. 2020; 8: 199539-199561. doi: 10.1109/access.2020.3035026
20. Abdulhadi N, Al-Mousa A. Diabetes Detection Using Machine Learning Classification Methods. In: Proceedings of the 2021 International Conference on Information Technology (ICIT); 14-15 July 2021; Amman, Jordan. pp. 350-354. doi: 10.1109/icit52682.2021.9491788
21. Ahmed U, Issa GF, Khan MA, et al. Prediction of Diabetes Empowered with Fused Machine Learning. IEEE Access. 2022; 10: 8529-8538. doi: 10.1109/access.2022.3142097
22. Manikandababu CS, IndhuLekha S, Jeniefer J, et al. Prediction of Diabetes using Machine Learning. In: Proceedings of the 2022 International Conference on Edge Computing and Applications (ICECAA); 13-15 October 2022; Tamilnadu, India. doi: 10.1109/icecaa55415.2022.9936375
23. Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021; 7(4): 432-439. doi: 10.1016/j.icte.2021.02.004
24. Hasan MdK, Alam MdA, Das D, et al. Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers. IEEE Access. 2020; 8: 76516-76531. doi: 10.1109/access.2020.2989857
25. Zou Q, Qu K, Luo Y, et al. Predicting Diabetes Mellitus With Machine Learning Techniques. Frontiers in Genetics. 2018; 9. doi: 10.3389/fgene.2018.00515
26. Maniruzzaman Md, Kumar N, Menhazul Abedin Md, et al. Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm. Computer Methods and Programs in Biomedicine. 2017; 152: 23-34. doi: 10.1016/j.cmpb.2017.09.004
27. Jackins V, Vimal S, Kaliappan M, et al. AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. The Journal of Supercomputing. 2020; 77(5): 5198-5219. doi: 10.1007/s11227-020-03481-x
28. Sneha N, Gangil T. Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big Data. 2019; 6(1). doi: 10.1186/s40537-019-0175-6
29. Bhaskar MA, Dash SS, Das S, et al. International Conference on Intelligent Computing and Applications. Springer Singapore; 2019. doi: 10.1007/978-981-13-2182-5
30. Sisodia D, Sisodia DS. Prediction of Diabetes using Classification Algorithms. Procedia Computer Science. 2018; 132: 1578-1585. doi: 10.1016/j.procs.2018.05.122
31. Orabi KM, Kamal YM, Rabah TM. Early Predictive System for Diabetes MellitusDisease. In: Proceedings of the Industrial Conference on Data Mining. Springer; 2017. pp. 420-427.
32. Baliunas DO, Taylor BJ, Irving H, et al. Alcohol as a Risk Factor for Type 2 Diabetes. Diabetes Care. 2009; 32(11): 2123-2132. doi: 10.2337/dc09-0227
33. Vazquez G, Duval S, Jacobs DR, et al. Comparison of Body Mass Index, Waist Circumference, and Waist/Hip Ratio in Predicting Incident Diabetes: A Meta-Analysis. Epidemiologic Reviews. 2007; 29(1): 115-128. doi: 10.1093/epirev/mxm008
34. Odegaard AO, Koh WP, Butler LM, et al. Dietary Patterns and Incident Type 2 Diabetes in Chinese Men and Women. Diabetes Care. 2011; 34(4): 880-885. doi: 10.2337/dc10-2350
35. Smith AD, Crippa A, Woodcock J, et al. Physical activity and incident type 2 diabetes mellitus: a systematic review and dose–response meta-analysis of prospective cohort studies. Diabetologia. 2016; 59(12): 2527-2545. doi: 10.1007/s00125-016-4079-0
36. Pan A, Wang Y, Talaei M, et al. Relation of active, passive, and quitting smoking with incident type 2 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2015; 3(12): 958-967. doi: 10.1016/S2213-8587(15)00316-2
37. Juneja A, Juneja S, Kaur S, et al. Predicting Diabetes Mellitus with Machine Learning Techniques Using Multi-Criteria Decision Making. International Journal of Information Retrieval Research. 2021; 11(2): 38-52. doi: 10.4018/ijirr.2021040103
38. Tigga NP, Garg S. Prediction of Type 2 Diabetes using Machine Learning Classification Methods. Procedia Computer Science. 2020; 167: 706-716. doi: 10.1016/j.procs.2020.03.336
39. Priyanka S, Kavitha C, Kumar MP. Deep Learning based Approach for Prediction of Diabetes. In: Proceedings of the 2023 2nd International Conference for Innovation in Technology (INOCON); 3-5 March 2023; Bangalore, India. pp. 1-6. doi: 10.1109/inocon57975.2023.10101241
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Prosanjeet Jyotirmay Sarkar, Satyanarayana Chanagala, George Chellin Jeya Chandra, Usha Ruby, Kavitha Manda
License URL: https://creativecommons.org/licenses/by/4.0/
Prof. Maode Ma
Qatar University, Qatar
Processing Speed
-
-
-
- ＜5 days from submission to initial review decision;
- 63% acceptance rate
-
-
The field of computer and telecommunications engineering is rapidly advancing, with the following being some of the latest developments.
more
We are pleased to congratulate the first anniversiry of the journal of Computer and Telecommunication Engineering (CTE).
more
The publication frequency of the journal will transition from a semi-annual to a quarterly schedule, publishing four issues per year (March, June, September, December), starting with the first issue of 2024.
more
The version of this guidence does not pretend to be either complete or neutral. It is intended to deal with some common errors and define the general criteria in Journal of Computer and Telecommunication Engineering .
The new instruction will be applied since 2023, and please authors follow this important notice
Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.