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Table 2 Studies dealing with SC models and allergic diseases

From: The soft computing-based approach to investigate allergic diseases: a systematic review

Authors Application Subjects Description Input features SC model Findings
Prosperi et al. [6] To predict asthma severity 383 children with asthma (age 6–18 years) Use of unsupervised statistical learning techniques, such as exploratory factor analysis (EFA), hierarchical clustering (HC) to identify asthma phenotypes Lung function, inflammatory and allergy markers, family history, environmental exposures, body mass index, age of asthma onset and medications BN Significant recognition of asthma severity
Farion et al. [12] To predict asthma exacerbation 322 Phase 1: 240 children (age 1–17 years)
Phase 2: 82 children (age 1–17 years)
Phase 1: selection of the most accurate machine learning model with WEKA tool
Phase 2: comparison of performance of BN with PRAM score and Physicians
42 attributes corresponding to the patient’s history, current asthma exacerbation, primary assessment and a selected secondary assessment BN Phase 1: Accuracy = 68%
Phase 2: Accuracy = 70.7%
Finkelstein et al. [13] To predict asthma exacerbation 26 adult asthmatic patients Use of a modern tele-monitoring system at home Daily self-reports SVM Accuracy = 80%
Sensitivity = 84%
Specificity = 80%
Sanders et al. [10] To predict asthma exacerbation 4023 patients (age 2–18 years) Use of a SC model to identify patients eligible for asthma care guidelines Past diagnoses, allergies, family history, medications, social history and vital signs (temperature, respiratory rate, and oxygen saturation) BN Accuracy = 96%
Sensitivity = 90%
Specificity = 88.3%
Dexheimer et al. [11] Same of Sanders et al. [10] 4023 patients (age 2–18 years) Comparison of machine learning models to best identify patients eligible for an asthma care guideline Same of Sanders et al. [10] ANN,BN, Gaussian processes (GP) BN accuracy = 96%
GP accuracy = 95.6%
ANN accuracy = 94%
Pifferi et al. [20] To classify asthma control levels 77 patients (age 7.5–17 years) Assessment of spirometry and fractional exhaled nitric oxide (FeNO) measurements to classify asthma control according to GINA guidelines 1st model: values of spirometry;
2nd model: values of FeNO;
3rd model: values of spirometry and FeNO
ANN 3rd model achieved best performances of classification
Accuracy = 86.4%
Pifferi et al. [17] To classify asthmatic vs control 123
90 asthmatic children (age 9–16 years) and 33 controls (age 12–13 years),
Pattern recognition analysis of the exhaled breath temperature curve The rate of temperature increase and the mean plateau value ANN Accuracy = 5%
Sensitivity = 77.2%
Specificity = 99%
Jaing et al. [21] To classify how children manage their asthma 305 children (age 5–14 years) Each participant was given 10 asthma-based problems and asked to manage them Each management decision and its order ANN Significant classification of five major classes representing different approaches to solving an acute asthma case
Kharroubi et al. [24] To classify health state of patient with asthma 307 subjects Estimation of a preference-based index for asthma (five-dimensional asthma quality of life utility index) 99 features about health statuses BN BN model is more appropriate than conventionally used parametric random-effects model
Hirsch et al. [4] To classify asthmatic vs control 6825 adults (age ≥ 16) Respiratory questionnaires were analyzed by experts and compared with results provided by neural network 12 answers provided by the respiratory questionnaire (wheezing, chest tightness, shortness of breath, night cough), family history of asthma and associated
conditions of hay fever or eczema
ANN Accuracy = 74%
Chatzimichail et al. [19] To classify asthmatic vs control 112 children (age 7–14 years) Three step analysis:1-feature selection with Principal Component Analysis, 2-pattern classification, 3-performance evaluation 46 prognostic factors including data on asthma, allergic diseases,
and lifestyle factors
SVM Accuracy = 95.54%
Sensitivity = 95.45%
Specificity = 95.59%
Goulart et al. [32] To classify allergic conjunctivitis vs control 102
48 with allergic conjunctivitis and 54 controls (age 3–14 years)
Allergic conjunctivitis questionnaires were analyzed by experts and compared with results provided by neural network 7 items selected from a questionnaire of 15 answers ANN Accuracy = 100%
Takahashi et al. [33] To classify atopic dermatitis vs control 4610 answers, 2714 infants (12 months old) and 1896 children (2 years old) To analyze the predictive accuracy of the predictive model for effect of atopic dermatitis in infancy, from the data of the epidemiological survey Family history (father, mother, siblings, grand-father, grand-mother), food restriction, food allergy, age, food restriction of mother, egg introduced time, cow’s milk introduced time ANN Accuracy = 96.4%
Sensitivity = 88.6%
Specificity = 99.5%
Christopher et al. [31] To classify allergic rhinitis vs control 872 patients of all age groups Allergic rhinitis reports of intradermal skin tests were analyzed by experts and compared with results provided by neural network Patient’s history and 40 clinically relevant allergens ANN Accuracy = 88.31%
Sensitivity = 88.3%
Specificity = 88.2%
De Matas et al. [23] To predict the clinical effect of salbutamol 23
12 healthy volunteers and 11 mild asthmatics
In vivo and in vitro data of human subjects were analyzed using SC modeling Demographic data and urinary levels of salbutamol and metabolite ANN Accuracy = 83.5%
De Matas et al. [22] To predict the clinical effect of salbutamol 18 mild-moderate asthmatic patients SC modeling to predict the bronchodilator response at 10 (T10) and 20 (T20) min after receiving each of the 4 doses for each of the 3 different particle sizes Aerodynamic particle size (APS), body surface area (BSA), age, pre-treatment forced expiratory volume in one-second (FEV1), forced vital capacity, cumulative emitted drug dose and bronchodilator reversibility ANN Accuracy = 88%
Gandhi et al. [25] To predict hypersensitivity reaction 2458 reports concerning thrombotic events selected from AERS (adverse event reporting system) database Retrospective analysis focused on thrombotic events associated with C1 esterase inhibitor products Adverse events; demographic and administrative information; drug/biologic information; report sources; patient outcomes; drug therapy start and end dates; indications for use/diagnosis BN Potential signals of C1 esterase inhibitor product—associated thrombotic events among patients with hereditary angioedema were identified
Naranjo et al. [28] To predict the posterior probability of a drug (BARDI tool) 51 patients BARDI tool, calculates the posterior probability of a drug being the cause based on epidemiologic and case data Reactions after receiving aromatic anticonvulsants BN Accuracy = 93%
Sensitivity = 94%
Specificity = 50%
Lanctot et al. [29] To predict the posterior probability of a drug (BARDI tool) 27 cases of skin reactions BARDI, combined with the LTA, a biochemical test that determines the percent of cell death because of toxic metabolites of a drug Skin reactions associated with sulfonamide therapy BN Accuracy = 96%
Sensitivity = 79%
Specificity = 38%
Lanctot et al. [30] To predict the posterior probability of a drug (BARDI tool) 106 challenging cases BARDI, compared with the Adverse Drug Reaction Probability Scale (APS) Drug- and nondrug-induced adverse events BN BN model discriminate better than ADR drug from nondrug-induced cases.
Kadoyama et al. [26] To predict hypersensitivity reaction caused by anticancer agents 1,644,220 reported cases from 2004 to 2009 (AERS database) SC model to detect important pattern related to anticancer agents-associated adverse events Adverse events; demographic and administrative information; drug/biologic information; report sources; patient outcomes; drug therapy start and end dates; indications for use/diagnosis BN Potential signals were detected for paclitaxel-associated mild, severe, and lethal hypersensitivity reactions, and docetaxel-associated lethal reactions
Sakaeda et al. [27] To predict hypersensitivity reactions caused by platinum agents 1,644,220 reported cases from 2004 to 2009 (AERS database) The BN analysis aims to search for previously unknown patterns and automatically detect important signals, i.e., platinum agent-associat d adverse events, from such a large database Adverse events; demographic and administrative information; drug/biologic information; report sources; patient outcomes; drug therapy start and end dates BN Significant association between the platinum agent-and mild, severe, and lethal hypersensitivity reactions
Lurie et al. [16] To classify asthma severity 113
patients (age 42.9 ± 16.3 years)
Implementation of a fuzzy model able to combine patients’ and doctors’ asthma perceptions Doctor assessment, variables self-assessed by patients (dyspnea, perceived treatment efficacy, asthma-related quality of life questionnaire (AQLQ)), patients’ sociodemographic characteristics, and asthma characteristics FL Accuracy = 73%
Zolnoori et al. [8] To classify asthma control level 42 asthmatic patients Implementation of a fuzzy model able to estimate the level of asthma control and help physicians to manage their patients more effectively Respiratory symptom severity, bronchial obstruction, asthma instability, current treatment and quality of life FL Accuracy = 100%
Zolnoori et al. [14] To classify asthma exacerbation 25 patients Implementation of a fuzzy model able to estimate the level of asthma exacerbation and help physicians to manage their patients more effectively Status of breathless, status of wheeze, status of alertness, status of respiratory rate, status of talk, status of pulse/min heart rate, value of PEF after initial bronchodilator, value of paCO2, value of SaO2% FL Accuracy = 100%
Zolnoori et al. [15] To classify asthma severity 28 patients Implementation of a fuzzy model able to estimate the four categories of asthma severity and help physicians to manage their patients more effectively Bronchial obstruction, response to drug, skin prick test, severity of respiratory symptoms, instability of asthma, IgE value, quality of life FL Accuracy = 100%
Zolnoori et al. [18] To classify asthmatic vs control 278
139 asthmatic patients and 139 non-asthmatic patients (age range 6–18)
Implementation of a fuzzy model to help physicians to manage their patients more effectively Medical history, environmental factors, allergic rhinitis, genetic factors, consequences of asthma on lung tissues, response to laboratory tests and response to short-term medicine FL Sensitivity = 88%
Specificity = 100%
  1. SC soft computing, ANN artificial neural networks, SVM support vector machines, BN Bayesian networks, FL Fuzzy logic, PRAM pediatric respiratory assessment measure. GINA global initiative for asthma