TY  - JOUR
AU  - Hosseini, Seyed Ahmad
AU  - Jamshidnezhad, Amir
AU  - Zilaee, Marzie
AU  - Fouladi Dehaghi, Behzad
AU  - Mohammadi, Abbas
AU  - Hosseini, Seyed Mohsen
PY  - 2020
DA  - 2020/7/6
TI  - Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study
JO  - JMIR Med Inform
SP  - e17580
VL  - 8
IS  - 7
KW  - asthma
KW  - machine learning
KW  - clinical predictor system
KW  - neural networks
KW  - supplement therapy
KW  - saffron
KW  - Crocus sativus L
AB  - Background: Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. Objective: The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. Methods: A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results: The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). Conclusions: The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2020/7/e17580
UR  - https://doi.org/10.2196/17580
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32628613
DO  - 10.2196/17580
ID  - info:doi/10.2196/17580
ER  -