ArticleBMC medical informatics and decision making2015
Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.BMC medical research methodology · 2025Pooled it
- A Multimodal Fuzzy Approach in Evaluating Pediatric Chronic Kidney Disease Using Kidney Biomarkers.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence in respiratory care.Frontiers in digital health · 2024Review
- A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
- Artificial intelligence to differentiate asthma from COPD in medico-administrative databases.BMC pulmonary medicine · 2022Article
- The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings.Journal of thoracic disease · 2021Article
- Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks.Medical & biological engineering & computing · 2021Article
- A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.Journal of medical systems · 2021Article
- Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?Frontiers in physiology · 2021Article
- Personalized medicine for patients with COPD: where are we?International journal of chronic obstructive pulmonary disease · 2019Review
- Acoustic Methods for Pulmonary Diagnosis.IEEE reviews in biomedical engineering · 2019Review
- A telehealth system for automated diagnosis of asthma and chronical obstructive pulmonary disease.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings.Scientific reports · 2018Article
- Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics.BMC medical genomics · 2018Article
- Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.International journal of chronic obstructive pulmonary disease · 2018Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network.
methodsFuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo.
resultsOut of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained.
conclusionOur neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification. Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patient's dynamic assessment, as opposed to the solution that provides a static assessment of the patient.
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