Application of Deep Learning Models into the Prediction of Interleukin-6 and -8 Cytokines in Sickle Cell Anemia Patients

Overview

Interleukin-6 (IL -6) and Interleukin-8 (IL -8) are cytokines related to general immune function, but within Sickle Cell Anemia (SCA) patients, their overproduction tends to cause autoimmune reactions. These vital cytokines engage in the pathophysiology of SCA, but the extent to which they’re associated with the disease’s genetics needs further exploration. This research paper seeks to further the study of IL -6 and IL -8 in SCA patients and the possibilities of predicting their presence in patients based on Haptoglobin alleles and various other hematological factors using artificial neural networks and deep learning techniques. This was done through a cross-sectional study of 60 sickle cell anemia patients and 74 healthy individuals who provided the basis of this study’s data. The data was used to build a machine learning model that would predict levels of the IL -6 and IL -8 cytokines. The deep learning model found a non-linear correlation between the Haptoglobin alleles and the production of IL -6 and IL -8, predicting their over levels in patients with an accuracy of 90.9% and r-squared value of 0.88 based on the given inputs. The machine learning models built in this paper have the potential to accelerate the development of targeted treatments and diagnoses to those suffering from Sickle Cell Anemia and its specific immune complications. WASHINGTON

Competition history

• JSHS 2023 Category not listed

Resources

• National JSHS abstract book ↗

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