English Українська
  • Main
  • Useful links
  • Information for Contributors
  • About
  • Editorial board

  • Article
    A. R. Khasawneh, S. V. Dmytrenko, Yu. G. Shevchuk, A. I. Kondratiuk, T. G. Kryvonis

    DISCRIMINANT MODELS OF SEBORRHEIC DERMATITIS POSSIBILITIES OF OCCURRENCE AND COURSE IN MEN AND WOMEN DEPENDING ON THE CHARACTERISTICS OF ANTHRO-SOMATOTYPOLOGICAL PARAMETERS


    About the author: A. R. Khasawneh, S. V. Dmytrenko, Yu. G. Shevchuk, A. I. Kondratiuk, T. G. Kryvonis
    Heading CLINICAL MEDICINE
    Type of article Scentific article
    Annotation Based on anthropometric indicators developed reliable discriminant models that allow to predict with high probability the possibility of generalized fatty seborrheic dermatitis in Ukrainian men and women (respectively correctness of 87.7 % and 91.8 % of cases, Wilks' Lambda statistics=0.063 and 0.174). The constructed models in men most often include girth (42.8 %) and thickness of skin and fat folds (28.6 %); in women – the thickness of skin and fat folds (42.8 %) and body diameters (28.6 %). In the constructed models, the greatest contribution to discrimination in men is made by the shoulder girths in the tense and unstressed state, and in women – the thickness of the skin and fat folds on the thighs and the width of the shoulders.
    Tags skin diseases, seborrheic dermatitis, Ukrainian men and women, body structure and size, anthropometry, discriminant analysis
    Bibliography
    • Dmytrenko SV, Maievskyi ОYe, Makarchuk IM. Dyskrymіnantnі modelі mozhlivostі zakhvoryuvannya ta osoblivostey perebіgu vugrovoyi khvorobi u dіvchat podіlskogo regіonu Ukrayiny v zalezhnostі vіd rozmіrіv tіla. Svіt medytsyny ta bіolohіyi. 2016;4(58):30–3. [in Ukrainian]
    • Kalmin OV, Galkina TN. Meditsinskaya antropologiya. Vysshee obrazovaniye: Specialitet, 2020. [in Russian]
    • Lavrushko SI, DudchenkoMO, Pavlenko HP, Myrronenko LV. Klіnіchnyi vypadok ta lіkuvannya seboreynoho dermatytu і kandydozu nіgtіv kistі. Ukrayinskyi zhurnal dermatolohіyi, venerolohіyi, kosmetolohіyi. 2020; 1(76):81–5. doi: 10.30978/UJDVK2020-I-81 [in Ukrainian]
    • Makarchuk IM, Maievskyi ОYe, Gunas IV. Modelyuvannya za dopomohoyu dyskrymіnantnoho analіzu mozhlivostі zakhvoryuvannya ta osoblivostey perebіhu vugrovoyi khvoroby v yunakіv Podіllya. Vіsnyk morfolohіyi. 2016;22(1):160–63. [in Ukrainian]
    • Chaplyk-Chyzho ІО. Modelyuvannya za dopomohoyu dyskrymіnantnoho analіzu mozhlivostі zakhvoryuvannya na pіodermіyu cholovіkіv і zhіnok zalezhno vіd osoblyvostey budovy ta rozmіrіv tіla. Biomedical and Biosocial Anthropology. 2016;26:68–71. [in Ukrainian]
    • Alamolhoda M, Heydari ST, Ayatollahi SMT, Tabrizi R, Akbari M, Ardalan A. A multivariate multilevel analysis of the risk factors associated with anthropometric indices in Iranian mid-adolescents. BMC Pediatr. 2020; 20(1):1–9. doi: 10.1186/s12887-020-02104-x
    • Emam S, Du AX, Surmanowicz P, Thomsen SF, Greiner R, Gniadecki R. Predicting the long-term outcomes of biologics in patients with psoriasis using machine learning. British Journal of Dermatology. 2020; 182(5):1305–7. doi: 10.1111/bjd.18741
    • Errichetti ENZO, Zalaudek I, Kittler H, Apalla Z, Argenziano G, Bakos R, et al. Standardization of dermoscopic terminology and basic dermoscopic parameters to evaluate in general dermatology (non-neoplastic dermatoses): an expert consensus on behalf of the International Dermoscopy Society. British Journal of Dermatology. 2020; 182(2):454–67. doi: 10.1111/bjd.18125
    • Gupta C, Gondhi NK, Lehana PK. Analysis and identification of dermatological diseases using Gaussian mixture modeling. IEEE Access, 2019; 7:99407–27. doi: 10.1109/ACCESS.2019.2929857
    • Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nature medicine. 2020;26(6):900–8. doi: 10.1038/s41591-020-0842-3
    • Markelova EM. Features of management of patients with seborrheic dermatitis. Medical alphabet. 2021;9:29–32. doi: 10.33667/2078-5631-2021-9-29-32
    • Obadeh Bassam Abdel-Rahman Al-Qaraleh, Dmytrenko SV, Kyrychenko VI, Datsenko GV, Gunas VI. Discriminant models of the possibility of occurrence and course of psoriasis in men of the general group and different somatotypes depending on the characteristics of anthro-somatotypological indicators.Reports of Morphology. 2021;27(3):67–72. doi: 10.31393/morphology-journal-2021-27(3)-10
    • Sudha M, Poorva B. Predictive Tool for Dermatology Disease Diagnosis using Machine Learning Techniques. IJITEE International Journal of Innovative Technology and Exploring Engineering. 2019;8(9):355–60. doi: 10.35940/ijitee.G5376.078919
    • Zander N, Sommer R, Schäfer I, Reinert R, Kirsten N, Zyriax BC, et al. Epidemiology and dermatological comorbidity of seborhoeic dermatitis: population-based study in 161–269 employees. British Journal of Dermatology. 2019;181(4):743–8. doi: 10.1111/bjd.17826
    • Zhu CY, Wang YK, Chen HP, Gao KL, Shu C, Wang JC, et al. A deep learning-based framework for diagnosing multiple skin diseases in a clinical environment. Frontiers in medicine. 2021; 8:626369. doi: 10.3389/fmed.2021.626369
    Publication of the article «World of Medicine and Biology» №2(80), 2022 year, 174-177 pages, index UDK 616.53-008.811.1-037-084-036.1:616-071.2
    DOI 10.26724/2079-8334-2022-2-80-174-177