Main Article Content
To break down the frequency and related risk elements of sorrow in patients with type 2 diabetes mellitus in the nearby local area and give logical references to clinical anticipation and treatment of diabetes mellitus with wretchedness. Proposed strategies use AI more than 58 patients with type 2 diabetes mellitus were chosen by efficient inspecting, and pertinent surveys examined segment factors and related clinical variables and misery sub-scale (PHQ) was utilized to assess the level of sadness. Social help scale was utilized to survey the patients. SSRS are utilized to assess individual social help levels and lead measurable investigation. After assessment it's seen that among the 58 patients with type 2 diabetes, the rate of consolidated melancholy was 58%; the age, conjugal status, training level, occupation, family ancestry, term of diabetes, entanglements, work out. The distinction in friendly help was genuinely critical. The impacting elements of type 2 diabetes confounded with gloom incorporate age, conjugal status, training level, occupation, and family ancestry, span of diabetes, presence or nonappearance of confusions, exercise and social help. They have a high gamble of muddled sorrow and influence the improvement of diabetes.
R. M. Khalil and A. Al-Jumaily, "Machine learning based prediction of depression among type 2 diabetic patients," 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017, pp. 1-5,DOI: 10.1109/ISKE.2017.8258766.
M. Mazandarani and A. V. Kamyad, "A practical approach to prescribe the amount of used insulin of diabetic patients," 2011 19th Iranian Conference on Electrical Engineering, 2011, pp. 1-1.
E. A. Koroleva and R. S. Khapaev, "The prevalence and risk factors of carotid artery stenosis in type 2 diabetic patients," 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB), 2020, pp. 253-256,DOI: 10.1109/CSGB51356.2020.9214707.
C. O. Iatcu, L. Cosman, M. Dimian and M. Covasa, "Dietary Patterns in Type-2 Diabetic Patients from Norheastern Romania," 2019 E-Health and Bioengineering Conference (EHB), 2019, pp. 1-4,DOI: 10.1109/EHB47216.2019.8970056.
S. b. Usman, M. A. b. M. Ali, M. M. B. I. Reaz and K. Chellapan, "Second derivative of photoplethysmogram in estimating vascular aging among diabetic patients," 2009 International Conference for Technical Postgraduates (TECHPOS), 2009, pp. 1-3,DOI: 10.1109/TECHPOS.2009.5412099.
P. Sharma and A. K. Shukla, "Analysis of Various Techniques and Methods for the Prediction of Diabetic Eye Disease in Type 2 Diabetes," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 1023-1030,DOI: 10.1109/ICIRCA51532.2021.9544622.
S. Zhu, H. Liu, R. Du, D. S. Annick, S. Chen and W. Qian, "Tortuosity of Retinal Main and Branching Arterioles, Venules in Patients With Type 2 Diabetes and Diabetic Retinopathy in China," in IEEE Access, vol. 8, pp. 6201-6208, 2020,DOI: 10.1109/ACCESS.2019.2963748.
L. Yousefi, S. Swift, M. Arzoky, L. Saachi, L. Chiovato and A. Tucker, "Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling," 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, pp. 1040-1044,DOI: 10.1109/BIBM.2018.8621484.
N. E. Myakina, I. A. Lots and V. V. Klimontov, "Continuous Glucose Monitoring Data Analysis in Insulin-Treated Type 1 And Type 2 Diabetic Subjects with the Use of Original Software," 2018 11th International Multiconference Bioinformatics of Genome Regulation and StructureSystems Biology (BGRSSB), 2018, pp. 24-27,DOI: 10.1109/CSGB.2018.8544878.
S. Usman, N. A. Bani, H. Mad Kaidi, S. A. MohdAris, S. Zura A. Jalil and M. N. Muhtazaruddin, "Second Derivative and Contour Analysis of PPG for Diabetic Patients," 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2018, pp. 59-62,DOI: 10.1109/IECBES.2018.8626681.
A. Christen, "Arterial evaluation in type 2 diabetes mellitus," 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010, pp. 3584-3584,DOI: 10.1109/IEMBS.2010.5627474.
Z. Darabi, M. H. F. Zarandi, S. S. Solgi and I. B. Turksen, "An intelligent multi-agent system architecture for enhancing self-management of type 2 diabetic patients," 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015, pp. 1-8,DOI: 10.1109/CIBCB.2015.7300273.
P. Hsu, H. Wang and H. Wu, "Poincaré plot indexes of pulse rate variability capture dynamic adaptations after reactive hyperemia in type 2 diabetic patients," 2012 IEEE International Conference on Electron Devices and Solid State Circuit (EDSSC), 2012, pp. 1- 4,DOI: 10.1109/EDSSC.2012.6482874.
L. Quintero et al., "Stress ECG and Laboratory Database for the Assessment of Diabetic Cardiovascular Autonomic Neuropathy," 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 4339-4342,DOI: 10.1109/IEMBS.2007.4353297.
R. Seki, K. Yoshino, K. Yana and T. Ono, "A method for characterizing circadian changes in QT intervals of diabetic patients," 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 1941-1944,DOI: 10.1109/IEMBS.2011.6090548.
S. Usman, M. Bin IbneReaz and M. A. M. Ali, "Risk prediction of having increased arterial stiffness among diabetic patients using logistic regression," 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016, pp. 699-701,DOI: 10.1109/IECBES.2016.7843540.
B. M. Patil, R. C. Joshi and D. Toshniwal, "Association Rule for Classification of Type-2 Diabetic Patients," 2010 Second International Conference on Machine Learning and Computing, 2010, pp. 330-334,DOI: 10.1109/ICMLC.2010.67.