Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

Abstract

<jats:sec> <jats:title>Background</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>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).</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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.</jats:p> </jats:sec>

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

“…In addition to the two aforementioned studies 45 , 46 , the effects of anti-inflammatory and antioxidative saffron on the treatment of mild-to-moderate allergic asthma in 80 patients were predicted using a genetic algorithm developed by modifying an artificial neural network system. The accuracy of the prediction system was greater than 99% in both the training and testing phase 69 , which probably makes it suitable for predicting the treatment effect of other asthma drugs. Nevertheless, the performance of this prediction system needs to be confirmed with studies on more patients with allergic or other types of asthma.…”

“…The primary limitation is the underlying model's lack of transparency. [32,[53][54][55][56], [62,69,79], [94,102] Latent class analysis Latent class analysis is a statistically principled technique that is used in factor analysis, cluster analysis, and regression. It is to explain and estimate the association between manifest indicators by latent class variables.…”

Saffron

“…109,110 Although there are anecdotal reports of allergic responses to saffron in consumers, the allergic potential of saffron is likely low. [111][112][113] Human trials that assessed oral intake of saffron or its constituents in amounts less than 400 mg/d showed no statistically significant differences in adverse effects, compared with controls. When evaluated in short-term human safety trials (7-30 days), saffron (30-400 mg/d), and crocin (20 mg/d) intakes were not associated with clinically significant changes in hematological, biochemical, hormonal, or urinary parameters, [114][115][116] although in 1 report, crocin dosing was associated with decreases in serum amylase and mixed white blood cells.…”

Effect of vitamin D supplementation on inflammatory markers and total antioxidant capacity in breast cancer women using a machine learning technique

“…The second layer is the hidden layer for learning, and the third layer is the output layer for presenting results. The advantage of the ANN method is the ability to use fewer data and input by providing the best results to help overcome medical limitations [27][28][29]. The ANNs operate based on experience and have a special strength to analyze and understand relationships between data, then modeling and inference.…”

“…The ANNs operate based on experience and have a special strength to analyze and understand relationships between data, then modeling and inference. The design of the ANN in analyzing the effects of clinical diets can be very advantageous [29].…”