Indian Journal of Dermatology
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E-IJD® - ORIGINAL ARTICLE
Year : 2022  |  Volume : 67  |  Issue : 3  |  Page : 311

Forecasting of atopic dermatitis in newborns


1 Department II of Children's Diseases, Azerbaijan Medical University, Baku, Republic of Azerbaijan
2 Department of Medical Physics and Informatics, Azerbaijan Medical University, Baku, Republic of Azerbaijan
3 Department of Premature Infants, Scientific-Research Institute of Pediatrics Named After K. Faradjeva, Baku, Republic of Azerbaijan

Correspondence Address:
Nurangiz Hajiyeva
Department II of Children's Diseases, Azerbaijan Medical University, AZ1022, 167 Samed Vurgun Str., Baku
Republic of Azerbaijan
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijd.ijd_933_21

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Background: Early forecasting of any pathological process is of great significance from both medical and economic point of view. An illness requires much more attention in the light of exhaustion of resources of the body, and a doctor should be maximally aware of the near and far future of a patient. In this regard, the preparation of forecasting programs on a mathematical basis would be a rational and, most probably, the only true approach to the solution of forecasting. Aims and Objectives: The aim of the article is to study the forecasting of atopic dermatitis (AD) in newborns. Methodology: The authors studied 109 clinical and laboratory indicators in children without and with AD. Discriminant analysis was used as an algorithm for the resolution of diagnostic issues. Results: The main indicators acceptable as a forecasting criterion in the formation of AD in children were defined. The sensitivity, specificity, and general diagnostic value of statistically valid differing factors in the formation of AD were studied. Key rules of the forecast were formed after processing all indicators through the KU–Kruskal–Wallis discriminant criterion, a universal computer method. Conclusion: It was concluded that the power of influence of rhinitis, cluster of differentiation 31, mucin 2, and intestinal trefoil factor 3 are higher in the AD model.


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