Artificial Immune System Implementation for Predicting WM Presence from MYD88 and CXCR4

Abstract

Waldenstrom’s Macroglobulinemia (WM) is a rare malignancy that affects human blood cells and spreads slowly. The development of WM occurs whenever the blood cells undergo genetic changes. Better therapies can be offered by the healthcare sector to get rid of the symptoms that cannot be cured. Everyone in the healthcare sector is aware that genetic abnormalities cause WM, but they are unsure of what causes the alterations. The risk factors that increase the number of WM's aberrant cells have been found. The greatest risk variables have a fatal impact on humans. The healthcare sector is working to save lives by offering better care. Only when WM is discovered earlier when it is treatable with better care and potent medications, is it very likely. For analysing the healthcare data associated with WM, a number of prior research studies have suggested both standard and unique software models and techniques. However, the accuracy is subpar and inefficient in terms of both time and money. To analyse the genomic dataset and detect Waldenstrom's Macroglobulinemia or its symptoms, this research explored this issue and suggested an Artificial Immune System (AIS) approach. Software written in Python is used to conduct the experiment and validate the findings. by contrasting the trial outcomes with other performance assessment techniques. The analysis reveals that the suggested AIS algorithm works better than the others.