Comparative Assessment of Machine Learning Algorithms for Effective Diabetes Prediction and Care
DOI:
https://doi.org/10.22399/ijcesen.606Keywords:
Artificial Intelligence, Machine Learning, Diabetic, PIMAAbstract
The prevalence and impact of diabetes have increased significantly over time, posing a major concern for the healthcare sector globally, especially in India. This study aims to enhance diabetes prediction and management through the use of artificial intelligence (AI) and machine learning (ML) methodologies. We present a range of AI-driven approaches that leverage ML algorithms to classify and predict diabetes more effectively. While most studies utilize the PIMA dataset, a few notable cases have also incorporated custom datasets curated from select healthcare organizations. This research provides a comparative assessment of state-of-the-art diabetes prediction methods alongside carefully selected care strategies. The study is organized into three categories, each exploring distinct approaches, and analyzes methodologies, ML algorithms, accuracy results, and validation metrics. By examining key parameters and techniques, this paper considers diabetes prediction and care tailored to the Indian population, accounting for various influencing factors.
References
Ali, A., & Iqbal, M.M. (2022). A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices based Applications in Mobile Cloud Computing. IEEE Access, 10;46633-46651, DOI: 10.1109/ACCESS.2022.3170918 DOI: https://doi.org/10.1109/ACCESS.2022.3170918
Castillo, O., Valdez, F., Soria, J., Yoon, J.H., Geem, Z.W., Peraza, C., Ochoa, P., & Amador-Angulo, L. (2020). Optimal Design of Fuzzy Systems Using Differential Evolution and Harmony Search Algorithms with Dynamic Parameter Adaptation. Applied Sciences. 10(18), 6146; https://doi.org/10.3390/app10186146 DOI: https://doi.org/10.3390/app10186146
Nguyen, T.A., Min, D., Choi, E., & Lee, J. (2021). Dependability and Security Quantification of an Internet of Medical Things Infrastructure Based on Cloud-Fog-Edge Continuum for Healthcare Monitoring Using Hierarchical Models. IEEE Internet of Things Journal, 8(21);15704-15748, doi: 10.1109/JIOT.2021.3081420. DOI: https://doi.org/10.1109/JIOT.2021.3081420
Kumar, N.G., & J, P.D. (2022). Shielded Identity Based Data and Contour Sharing for Mobile Healthcare Through Cloud Computing. International Journal for Research in Applied Science and Engineering Technology. 10(2);1250-1253 DOI: 10.22214/ijraset.2022.40497 DOI: https://doi.org/10.22214/ijraset.2022.40497
Chinnasamy, P., Albakri, A., Khan, M., Raja, A.A., Kiran, A., & Babu, J.C. (2023). Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System. Applied Sciences. 13(6);3970; https://doi.org/10.3390/app13063970 DOI: https://doi.org/10.3390/app13063970
Khan, M.A., & Algarni, F. (2020). A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS. IEEE Access, 8, 122259-122269. doi: 10.1109/ACCESS.2020.3006424 DOI: https://doi.org/10.1109/ACCESS.2020.3006424
Umak, R. (2021). Sharing Healthcare Records in the Cloud Using Attribute-Based Encryption and De-Duplication. International Journal for Research in Applied Science and Engineering Technology. 9(VII);3345-3350. DOI: 10.22214/ijraset.2021.37109 DOI: https://doi.org/10.22214/ijraset.2021.37109
M. N. Bajwa et al., “Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks,” Applied Sciences, 10(7);2488, doi: 10.3390/app10072488. DOI: https://doi.org/10.3390/app10072488
X. Zhang, S. Wang, J. Liu and C. Tao, "Computer-aided diagnosis of four common cutaneous diseases using deep learning algorithm," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 1304-1306, doi: 10.1109/BIBM.2017.8217850. DOI: https://doi.org/10.1109/BIBM.2017.8217850
Kumar, N.G., & Bhuvana J, P.D. (2022). Survey: Shielded Identity Based Data and Contour Sharing for Mobile Healthcare Through Cloud Computing. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2022.40936 DOI: https://doi.org/10.22214/ijraset.2022.40936
M. Husain Bathushaw, & S. Nagasundaram. (2024). The Role of Blockchain and AI in Fortifying Cybersecurity for Healthcare Systems. International Journal of Computational and Experimental Science and Engineering, 10(4);1120-1129. https://doi.org/10.22399/ijcesen.596 DOI: https://doi.org/10.22399/ijcesen.596
Rama Lakshmi BOYAPATI, & Radhika YALAVARTHI. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);879-889. https://doi.org/10.22399/ijcesen.519 DOI: https://doi.org/10.22399/ijcesen.519
AY, S. (2024). Vehicle Detection And Vehicle Tracking Applications On Traffic Video Surveillance Systems: A systematic literature review. International Journal of Computational and Experimental Science and Engineering, 10(4);1059-1068. https://doi.org/10.22399/ijcesen.629 DOI: https://doi.org/10.22399/ijcesen.629
Sheela Margaret D, Elangovan N, Sriram M, & Vedha Balaji. (2024). The Effect of Customer Satisfaction on Use Continuance in Bank Chatbot Service. International Journal of Computational and Experimental Science and Engineering, 10(4);1069-1077. https://doi.org/10.22399/ijcesen.410 DOI: https://doi.org/10.22399/ijcesen.410
jaber, khalid, Lafi, M., Alkhatib, A. A., AbedAlghafer, A. K., Abdul Jawad, M., & Ahmad, A. Q. (2024). Comparative Study for Virtual Personal Assistants (VPA) and State-of-the-Art Speech Recognition Technology. International Journal of Computational and Experimental Science and Engineering, 10(3);427-433. https://doi.org/10.22399/ijcesen.383 DOI: https://doi.org/10.22399/ijcesen.383
Guven, M. (2024). A Comprehensive Review of Large Language Models in Cyber Security. International Journal of Computational and Experimental Science and Engineering, 10(3);507-516. https://doi.org/10.22399/ijcesen.469 DOI: https://doi.org/10.22399/ijcesen.469
M, V., V, J., K, A., Kalakoti, G., & Nithila, E. (2024). Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support. International Journal of Computational and Experimental Science and Engineering, 10(4);575-584. https://doi.org/10.22399/ijcesen.479 DOI: https://doi.org/10.22399/ijcesen.479
ÖZNACAR, T., & ERGENE, N. (2024). A Machine Learning Approach to Early Detection and Malignancy Prediction in Breast Cancer. International Journal of Computational and Experimental Science and Engineering, 10(4)911-917. https://doi.org/10.22399/ijcesen.516 DOI: https://doi.org/10.22399/ijcesen.516
Türkmen, G., Sezen, A., & Şengül, G. (2024). Comparative Analysis of Programming Languages Utilized in Artificial Intelligence Applications: Features, Performance, and Suitability. International Journal of Computational and Experimental Science and Engineering, 10(3);461-469. https://doi.org/10.22399/ijcesen.342 DOI: https://doi.org/10.22399/ijcesen.342
Venkatraman Umbalacheri Ramasamy. (2024). Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review. International Journal of Computational and Experimental Science and Engineering, 10(4);898-910. https://doi.org/10.22399/ijcesen.522 DOI: https://doi.org/10.22399/ijcesen.522
Jafar Ismail, R., Samar Jaafar Ismael, Dr. Sara Raouf Muhamad Amin, Wassan Adnan Hashim, & Israa Tahseen Ali. (2024). Survey of Multiple Destination Route Discovery Protocols. International Journal of Computational and Experimental Science and Engineering, 10(3);420-426. https://doi.org/10.22399/ijcesen.385 DOI: https://doi.org/10.22399/ijcesen.385
guven, mesut. (2024). Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence. International Journal of Computational and Experimental Science and Engineering, 10(3);480-490. https://doi.org/10.22399/ijcesen.460 DOI: https://doi.org/10.22399/ijcesen.460
Serap ÇATLI DİNÇ, AKMANSU, M., BORA, H., ÜÇGÜL, A., ÇETİN, B. E., ERPOLAT, P., … ŞENTÜRK, E. (2024). Evaluation of a Clinical Acceptability of Deep Learning-Based Autocontouring: An Example of The Use of Artificial Intelligence in Prostate Radiotherapy. International Journal of Computational and Experimental Science and Engineering, 10(4);1181-1186. https://doi.org/10.22399/ijcesen.386 DOI: https://doi.org/10.22399/ijcesen.386
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