Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis, and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.
The main purpose of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, Bayesian networks, and fuzzy logic to investigate their performances in the field of allergic diseases.
PRISMA guidelines were followed for this review and the protocol was registered within the PROSPERO database. Online search was done on the medical web portals of PubMed and ScienceDirect.
27 studies were reviews that were related to allergic diseases and soft computing performances. The obtained findings revealed that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. They can provide valuable support in case of a lack of data and entangled cause-effect relationships, which make it difficult to assess the evolution of the disease.
The study concluded through the findings that although most works deal with asthma, still the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.
Reference: https://clinicalmolecularallergy.biomedcentral.com/articles/10.1186/s12948-017-0066-3
