Classification of Chronic Obstructive Pulmonary Disease (COPD) Using Integrated Software Suite

Badnjević, Almir; Cifrek, Mario; Koruga, Dragan

doi:10.1007/978-3-319-00846-2_225

Cited by 17 publications

(3 citation statements)

References 3 publications

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“…With the ongoing exploration of the relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD, a research direction of interest is to develop an artificial intelligence tool to assist in the diagnosis of depression in elderly patients with COPD. A telemetry system was developed to diagnose asthma and COPD [53], and other studies have investigated the use of various artificial intelligence systems and machine learning models in the diagnosis of various diseases [52,[54][55][56][57][58]. We anticipate that an artificial intelligence tool could assist patients with better self-management and enable physicians to rapidly diagnose depression in elderly patients with COPD, monitor patient health status, and potentially provide appropriate professional care or treatment.…”

Section: Discussionmentioning

confidence: 99%

Relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD and its clinical implications

Zhang,

Wang,

et al. 2023

THC

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BACKGROUND: Chronic obstructive pulmonary disease (COPD) causes organic damage as well as anxiety, depression, fear, and other psychological disorders, which seriously affect the quality of life and prognosis of patients and cause a huge economic burden to the family and society. OBJECTIVE: The aim of this study was to investigate the correlation between an imbalance of serum Th1/Th2 indicators and psychiatric depression in elderly patients with COPD and analyze its implications for clinical management. METHODS: From January 2018 to May 2022, 120 elderly patients with COPD treated at our hospital were categorized into two groups based on the self-rating depression scale (SDS): COPD with depression (SDS score ⩾ 50) and COPD alone (SDS score < 50). Blood gas analysis, pulmonary function, and serum Th1/Th2 index were determined. Receiver operating characteristic (ROC) curves were analyzed to explore the diagnostic value of serum Th1/Th2 ratios for COPD complicated by depression. RESULTS: Compared with the group without depression, the partial pressure of carbon dioxide and COPD assessment test scores were significantly higher, and the oxygenation index, forced expiratory volume in one second (FEV1), and percent predicted FEV1 were significantly lower in the COPD with depression group (P< 0.05). Interleukin (IL)-1β, IL-2, IL-6, IL-8, IL-10, and tumor necrosis factor-α (TNF-α) were significantly higher in the COPD with depression group than in the group without depression (P< 0.05). Logistic regression analysis indicated that the imbalance of serum IL-1β, IL-2, IL-6, IL-8, IL-10, and TNF-α was a risk factor for mental depression in elderly patients with COPD. When comparing prognostic indices, the interval before the first onset of clinically noticeable deterioration (CID-C) in the COPD with depression group was noticeably shorter than that in the COPD without depression group; the incidence of CID-C within 6 months was noticeably higher in the COPD with depression group than in the group without depression. CONCLUSION: Elderly patients with COPD and depression had reduced pulmonary function and higher serum Th1/Th2 levels, and an imbalance in serum Th1/Th2 indicators was a potential risk factor for depression. Moreover, elderly patients with COPD and depression were at a higher risk of disease progression and had a worse prognosis. Thus, an imbalance in serum Th1/Th2 indicators is a potential prognostic factor for evaluating depression in patients with COPD.

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Section: Discussionmentioning

confidence: 99%

Relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD and its clinical implications

Zhang,

Wang,

et al. 2023

THC

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“…In 2008, E. Meraz et al [ 17 ] and in 2009 N. Hafezi et al [ 18 ], based on known equivalent electrical models of lungs and their values specified for the healthy and diseased patients [ 42 - 49 ], developed a computational tool that classifies respiratory diseases in children by using IOS results. The advantage of our previous research [ 50 - 52 ] and new solution with respect to the one presented by E. Meraz et al [ 17 ] and N. Hafezi et al [ 18 ] is that our solution uses a combination of spirometry and IOS classification test results, which in the very beginning enables more accurate classification. Also, for the classification of diseases, in addition to the results obtained by using spirometry and IOS, symptoms according to GINA and GOLD rules as well as bronchiodilatory tests are necessary for proper classification of asthma and COPD.…”

Section: Discussionmentioning

confidence: 99%

Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease

Badnjević

Cifrek

Koruga³

et al. 2015

BMC Med Inform Decis Mak

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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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“…Badnjevic et al proposed a method using fuzzy rules and an artificial neural network (ANN) to classify COPD patient’s lung function. So, for this attempt 285 COPD patient’s data were used, and they found 92% accurate classification [ 44 ]. Barúa et al proposed a method using a feedforward artificial neural network (ANN) to classifying the patients who were affected by central and peripheral airways.…”

Section: Related Workmentioning

confidence: 99%

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

et al. 2021

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Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

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Classification of Chronic Obstructive Pulmonary Disease (COPD) Using Integrated Software Suite

Cited by 17 publications

References 3 publications

Relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD and its clinical implications

Relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD and its clinical implications

Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

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