From: The soft computing-based approach to investigate allergic diseases: a systematic review
Type of allergy | No. of studies | Overall accuracy (%) | Application | ANN | SVM | BN | FL |
---|---|---|---|---|---|---|---|
Asthma | 18 | 82.44 ± 23.71 | 1 | 1 | 2 | 1 | |
– | – | 1 | 2 | ||||
2 | 1 | – | 1 | ||||
1 | – | – | 1 | ||||
To classify how manage their pathology [21] | 1 | – | – | – | |||
2 | – | – | – | ||||
To classify health state[24] | – | – | 1 | – | |||
ADR | 6 | 94.5 ± 2.12 | To predict the posterior probability of a drug (BARDI tool) [28,29,30] | – | – | 3 | – |
To predict hypersensitivity reaction (AERS database) [25,26,27] | – | – | 3 | – | |||
Allergic rhinitis | 1 | 88.31 | To classify pathologic vs control [31] | 1 | – | – | – |
Allergic conjunctivitis | 1 | 100 | To classify pathologic vs control [32] | 1 | – | – | – |
Atopic dermatitis | 1 | 96.4 | To classify pathologic vs control [33] | 1 | – | – | – |