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RESEARCH ARTICLE

Analysis of risk factors in diabetics resulted from polycystic ovary syndrome in women by EDA analysis and machine learning techniques

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Pages 77-97 | Received 10 Mar 2023, Accepted 17 Aug 2023, Published online: 04 Sep 2023

References

  • Antony L, Azam S, Ignatious E, Quadir R, Beeravolu AR, Jonkman M, De Boer F. 2021. A comprehensive unsupervised framework for chronic kidney disease prediction. IEEE Access. 9:126481–126501. doi:10.1109/ACCESS.2021.3109168.
  • Arora N, Singh A, Al-Dabagh MZN, Maitra SK. 2022. A novel architecture for diabetes patients’ prediction using K-means clustering and SVM. Math Prob Eng. 2022:1–9. doi:10.1155/2022/4815521.
  • Ashraf S, Nabi M, Rasool S, Rashid F, Amin S. 2019. Hyperandrogenism in polycystic ovarian syndrome and role of CYP gene variants: a review. Egypt J Med Hum Genet. 20:25. doi:10.1186/s43042-019-0031-4.
  • Burghen GA, Givens JR, Kitabchi AE. 1980. Correlation of hyperandrogenism with hyperinsulinism in polycystic ovarian disease. J Clin Endocrinol Metab. 50(1):113–116. doi:10.1210/jcem-50-1-113.
  • Cui S, Wang Y, Yin Y, Cheng TCE, Wang D, Zhai M. 2021. A cluster-based intelligence ensemble learning method for classification problems. Inf Sci. 560:386–409. doi:10.1016/j.ins.2021.01.061.
  • Dadachanji R, Shaikh N, Mukherjee S. 2018. Genetic variants associated with hyperandrogenemia in PCOS pathophysiology. Genet Res Int. 2018:7624912–7624932. doi:10.1155/2018/7624932.
  • Das I. 2020. Polycystic ovary syndrome: dietary approaches to counteract insulin resistance. J Undergrad Life Sci. 14(1):3. doi:10.33137/juls.v14i1.35925.
  • Diamanti-Kandarakis E, Dunaif A. 2012. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr Rev. 33(6):981–1030. doi:10.1210/er.2011-1034.
  • Geffner ME, Golde DW. 1988. Selective insulin action on skin, ovary, and heart in insulin-resistant states. Diabetes Care. 11(6):500–505. doi:10.2337/diacare.11.6.500.
  • Guo S, Tal R, Jiang H, Yuan T, Liu Y. 2020. Vitamin D supplementation ameliorates metabolic dysfunction in patients with PCOS: a Systematic Review of RCTs and insight into the underlying mechanism. Int J Endocrinol. 2020:7850816. doi:10.1155/2020/7850816.
  • Guo X, Xu Y, Sun J, Wang Q, Kong H, Zhong Z. 2022. Exploring the mechanism of Wenshen Huatan Quyu Decoction for PCOS based on network pharmacology and molecular docking verification. Stem Cells Int. 2022:3299091. doi:10.1155/2022/3299091.
  • He Y, Hu W, Yang G, Guo H, Liu H, Li L. 2020. Adipose insulin resistance and circulating betatrophin levels in women with PCOS. Biomed Res Int. 2020:1253164. doi:10.1155/2020/1253164.
  • Herman R, Sikonja J, Jensterle M, Janez A, Dolzan V. 2023. Insulin metabolism in polycystic ovary syndrome: secretion, signaling, and clearance. Int J Mol Sci. 24(4):3140. doi:10.3390/ijms24043140.
  • Hong Y, Zhou ZH, Dong Z, Yang DZ. 2023. Prevalence of polycystic ovary syndrome under NIH criteria among the tenth-grade Chinese schoolgirls in Guangzhou area: a cross-sectional epidemiological survey. BMC Womens Health. 23(1):31.
  • Joksimovic Jovic J, Sretenovic J, Jovic N, Rudic J, Zivkovic V, Srejovic I, Mihajlovic K, Draginic N, Andjic M, Milinkovic M, et al. 2021. Cardiovascular properties of the androgen-induced PCOS model in rats: the role of oxidative stress. Oxid Med Cell Longev. 2021:8862878. doi:10.1155/2021/8862878.
  • Julania S, Walls ML, Hart R. 2018. The Place of In Vitro Maturation in PCO/PCOS. Int J Endocrinol. 2018:5750298–5750298. doi:10.1155/2018/5750298.
  • Khanna VV, Chadaga K, Sampathila N, Prabhu S, Bhandage V, Hegde GK. 2023. A distinctive explainable machine learning framework for detection of polycystic ovary syndrome. ASI. 6(2):32. doi:10.3390/asi6020032.
  • Kodipalli A, Devi S. 2021. Prediction of PCOS and mental health using fuzzy inference and SVM. Front Public Health. 9:789569. doi:10.3389/fpubh.2021.789569.
  • Koteeswaran S, Suganya R, Surianarayanan C, Neeba EA, Suresh A, Chelliah PR, Buhari SM. 2023. A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu. Soft Comput. 1–15. doi:10.1007/s00500-023-08590-2.
  • Li J, Huang J, Jiang T, Tu L, Cui L, Cui J, Ma X, Yao X, Shi Y, Wang S, et al. 2022. A multi-step approach for tongue image classification in patients with diabetes. Comput Biol Med. 149:105935. doi:10.1016/j.compbiomed.2022.105935.
  • Livadas S, Anagnostis P, Bosdou JK, Bantouna D, Paparodis R. 2022. Polycystic ovary syndrome and type 2 diabetes mellitus: a state-of-the-art review. World J Diabetes. 13(1):5–26. doi:10.4239/wjd.v13.i1.5.
  • Lizano P, Lutz O, Xu Y, Rubin LH, Paskowitz L, Lee AM, Eum S, Keedy SK, Hill SK, Reilly JL, et al. 2021. Multivariate relationships between peripheral inflammatory marker subtypes and cognitive and brain structural measures in psychosis. Mol Psychiatry. 26(7):3430–3443. doi:10.1038/s41380-020-00914-0.
  • Long C, Feng H, Duan W, Chen X, Zhao Y, Lan Y, Yue R. 2022. Prevalence of polycystic ovary syndrome in patients with type 2 diabetes: A systematic review and meta-analysis. Front Endocrinol (Lausanne). 13:980405. doi:10.3389/fendo.2022.980405.
  • Mehedi Hassan M, Mollick S, Yasmin F. 2022. An unsupervised cluster-based feature grouping model for early diabetes detection. Healthcare Analytics. 2(100112):100112. doi:10.1016/j.health.2022.100112.
  • Nagamani M, Van Dinh T, Kelver ME. 1986. Hyperinsulinemia in hyperthecosis of the ovaries. Am J Obstet Gynecol. 154(2):384–389. doi:10.1016/0002-9378(86)90676-9.
  • Nasim S, Almutairi MS, Munir K, Raza A, Younas F. 2022. A novel approach for polycystic ovary syndrome prediction using machine learning in bioinformatics. IEEE Access. 10:97610–97624. doi:10.1109/ACCESS.2022.3205587.
  • Nisenblat V, Norman RJ. 2009. Androgens and polycystic ovary syndrome. Curr Opin Endocrinol Diabetes Obes. 16(3):224–231. doi:10.1097/MED.0b013e32832afd4d.
  • Palmer MB, Abedini A, Jackson C, Blady S, Chatterjee S, Sullivan KM, Townsend RR, Brodbeck J, Almaani S, Srivastava A, et al. 2021. The role of glomerular epithelial injury in kidney function decline in patients with diabetic kidney disease in the TRIDENT cohort. Kidney Int Rep. 6(4):1066–1080. doi:10.1016/j.ekir.2021.01.025.
  • Poretsky L. 1991. On the paradox of insulin-induced hyperandrogenism in insulin-resistant states. Endocr Rev. 12(1):3–13. doi:10.1210/edrv-12-1-3.
  • Prashanthi B, Sowjanya G. 2020. Clustering techniques in medical analysis using deep representations. Int J Adv SciTechnol. 29(12s):2184–2189. http://sersc.org/journals/index.php/IJAST/article/view/24400
  • Rodgers RJ, Avery JC, Moore VM, Davies MJ, Azziz R, Stener-Victorin E, Moran LJ, Robertson SA, Stepto NK, Norman RJ, et al. 2019. Complex diseases and co-morbidities: polycystic ovary syndrome and type 2 diabetes mellitus. Endocr Connect. 8(3):R71–R75. doi:10.1530/EC-18-0502.
  • Sam S. 2007. Obesity and polycystic ovary syndrome. Obes Manag. 3(2):69–73. doi:10.1089/obe.2007.0019.
  • Sinha AA, Rajendran S. 2022. A novel two-phase location analytics model for determining operating station locations of emerging air taxi services. Decision Analytics Journal. 2(100013):100013. doi:10.1016/j.dajour.2021.100013.
  • Sowndarya. 2023. Diabetic-dataset. GitHub. [accessed 2023 Feb 2]. https://github.com/Sowndarya-23/Diabetic-dataset-.
  • Stridsklev S, Salvesen Ø, Salvesen KÅ, Carlsen SM, Vanky E. 2018. Uterine artery Doppler in pregnancy: women with PCOS compared to healthy controls. Int J Endocrinol. 2018:2604064. doi:10.1155/2018/2604064.
  • Sun J, Sun JA. 2016. Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model. IET Intell Transp Syst. 10(5):331–337. doi:10.1049/iet-its.2014.0288.
  • Syakur MA, Khotimah BK, Rochman EMS, Satoto BD. 2018. Integration K-means clustering method and elbow method for identification of the best customer profile cluster. IOP Conf Ser: mater Sci Eng. 336:012017. doi:10.1088/1757-899X/336/1/012017.
  • Thakare AD, Lele PR; Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India. 2020. Genetic clustering for polycystic ovary syndrome detection in women of reproductive age. IJEAT. 9(3):1356–1361. doi:10.35940/ijeat.C5457.029320.
  • Uçkan K, Demir H, Turan K, Sarıkaya E, Demir C. 2022. Role of oxidative stress in obese and nonobese PCOS patients. Int J Clin Pract. 2022:4579831. doi:10.1155/2022/4579831.
  • Xiang J, Chen Z. 2018. Traffic state estimation of signalized intersections based on stacked denoising auto-encoder model. Wireless Pers Commun. 103(1):625–638. doi:10.1007/s11277-018-5466-2.
  • Xie Q, Xiong X, Xiao N, He K, Chen M, Peng J, Su X, Mei H, Dai Y, Wei D, et al. 2019. Mesenchymal stem cells alleviate DHEA-induced polycystic ovary syndrome (PCOS) by inhibiting inflammation in mice. Stem Cells Int. 2019:9782373. doi:10.1155/2019/9782373.
  • Xing L, Chen Y, He Z, He M, Sun Y, Xu J, Wang J, Zhuang H, Ren Z, Chen Y, et al. 2022. Acupuncture improves endometrial angiogenesis by activating PI3K/AKT pathway in a rat model with PCOS. Evid Based Complement Alternat Med. 2022:1790041. doi:10.1155/2022/1790041.
  • Xu Y, Qiao J. 2022. Association of insulin resistance and elevated androgen levels with the polycystic ovarian syndrome (PCOS): a review of the literature. J Healthc Eng. 2022:9240569. doi:10.1155/2022/9240569.
  • Yu K, Wang R-X, Li M-H, Sun T-C, Zhou Y-W, Li Y-Y, Sun L-H, Zhang B-L, Lian Z-X, Xue S-G, et al. 2019. Melatonin reduces androgen production and upregulates heme oxygenase-1 expression in granulosa cells from PCOS patients with hypoestrogenia and hyperandrogenia. Oxid Med Cell Longev. 2019:8218650. doi:10.1155/2019/8218650.
  • Yufeng. 2022. Three performance evaluation metrics of clustering when ground truth labels are not available. Towards Data Science. [accessed 2023 Mar 7]. https://towardsdatascience.com/three-performance-evaluation-metrics-of-clustering-when-ground-truth-labels-are-not-available-ee08cb3ff4fb.
  • Zhao H, Zhang J, Cheng X, Nie X, He B. 2023. Insulin resistance in polycystic ovary syndrome across various tissues: an updated review of pathogenesis, evaluation, and treatment. J Ovarian Res. 16(1):9. doi:10.1186/s13048-022-01091-0.
  • Zigarelli A, Jia Z, Lee H. 2022. Machine-aided self-diagnostic prediction models for polycystic ovary syndrome: observational study. JMIR Form Res. 6(3):e29967. doi:10.2196/29967.

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