Machine Learning for the Diagnosis of Chronic Obstructive Pulmonary Disease and Photoplethysmography Signal – Based Minimum Diagnosis Time Detection

Melekoglu, Engin; Kocabıçak, Ümit; Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit

doi:10.1007/978-3-030-79357-9_6

Cited by 2 publications

(7 citation statements)

References 7 publications

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“…Cai et al 42 proposed a multivariate logistic regression‐based approach for diagnosing the relationship between the individual socioeconomic status variables and the prevalence, treatment of COPD. Melekoğlu et al 43 presented a novel decision tree‐based machine learning approach to detect and classify the different classes of COPD. This method provided 98.99% of classification accuracy and 0.99 sensitivity and specificity.…”

Section: Review Of Related Workmentioning

confidence: 99%

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

Siva

Saravanan

et al. 2021

Int J Imaging Syst Tech

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Pulmonary disease is a kind of disease that affects the lungs and other parts of the respiratory system and is mainly caused by smoking, asbestos, secondhand smoke, and other forms of air pollutants. Several types of pulmonary diseases are emphysema, fibrosis, pneumothorax, asthma, lung cancer, chronic obstructive pulmonary disease (COPD), and so on. Pulmonary diseases are otherwise known as lung disorders and respiratory diseases. Pulmonary diseases are predicted by several methods using x‐ray images and CT scan images. Some of the recent works predict only a specific disease, and optimal prediction is not yet achieved. Hence, we proposed a novel method known as African vulture optimization (AVO) algorithm‐based weighted support vector machine approach (w‐SVM). The proposed method in this article predicts emphysema, fibrosis, pneumothorax, and normal kinds from the NIH chest x‐ray dataset. The X‐images are preprocessed after data acquisition to obtain the desired size and to remove undesirable noise. The preprocessed images are then sent into the SVM for feature extraction, and the AVO is used to improve the SVM so that a kernel function may be obtained. The proposed w‐SVM effectively predicts the emphysema, fibrosis, pneumothorax, and normal classes from the dataset. The experimental analyses are conducted and compared with existing works and concluded that the proposed work outperforms other approaches in terms of accuracy, sensitivity, specificity, and Matthews's correlation coefficient, prediction time, and modeling time.

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Section: Review Of Related Workmentioning

confidence: 99%

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

Siva

Saravanan

et al. 2021

Int J Imaging Syst Tech

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“…Chronic obstructive pulmonary disease (COPD) is characterized by breathing signs and symptoms and airflow challenges because of anomalies in the airways and the alveoli that occurs as a result of significant exposure to harmful particles and gases. COPD is a widespread, preventable, and curable disease ( Zubaydi et al, 2017 ; Melekoğlu et al, 2021 ; Batum et al, 2015 ). COPD constitutes a significant portion of chronic respiratory diseases.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…A medical professional can make a diagnosis by comparing spirometry measurements with reference values determined by age, height, weight, and BMI. When we divide FEV1 by FVC, it is considered to be less than 70% a COPD patient ( Melekoğlu et al, 2021 ; Isik, Guven & Buyukoglan, 2015 ; Uçar et al, 2018b ). The difficulties of using the spirometer device can be experienced, especially in small children, the disabled, and patients with advanced illnesses.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…The difficulties of using the spirometer device can be experienced, especially in small children, the disabled, and patients with advanced illnesses. This necessitates shortening and facilitating the diagnosis time ( Melekoğlu et al, 2021 ; Er & Temurtas, 2008 ; Er et al, 2009 ). Because of these drawbacks, there is a need to design methods that are simple to use and follow in order to diagnose COPD more effectively ( Uçar et al, 2018b , 2018c ).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Because of these drawbacks, there is a need to design methods that are simple to use and follow in order to diagnose COPD more effectively ( Uçar et al, 2018b , 2018c ). To overcome these problems, in order to make the COPD diagnoses process faster and then patient monitoring easier, it is taken into consideration that the usage of the photoplethysmography (PPG) signal can be beneficial withinside the diagnosis of COPD ( Melekoğlu et al, 2021 ; Moraes et al, 2018 ). The PPG is a biological signal that may be measurable anywhere near the heart.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

Melekoglu

Kocabıçak

Uçar

et al. 2022

PeerJ Computer Science

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Background and Purpose Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD. Methods During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance. Results This improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase. Conclusion According to the findings, PPG-based COPD prediction models are suitable for usage in practice.

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Machine Learning for the Diagnosis of Chronic Obstructive Pulmonary Disease and Photoplethysmography Signal – Based Minimum Diagnosis Time Detection

Cited by 2 publications

References 7 publications

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

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