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Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using ne...
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Citations
... The thyroid gland regulates temperature, controls respiration, blood flow, stomach motions, muscular contractions, digestion, and brain activity. Normal physiological function of the human body may be impacted by thyroid gland abnormality (Er et al., 2009). ...
... The advent of ANNs, fueled by the power of deep learning, has paved the way for more precise and automated analysis of medical images. CNNs, a subset of ANNs, excel in capturing intricate patterns and features within images, enabling them to identify subtle anomalies indicative of various lung diseases as Figure 2 [6][7][8][9][10][11]. The cornerstone of the proposed system lies in its ability to learn complex representations from labeled datasets containing a diverse array of lung images. ...
... The advent of ANNs, fueled by the power of deep learning, has paved the way for more precise and automated analysis of medical images. CNNs, a subset of ANNs, excel in capturing intricate patterns and features within images, enabling them to identify subtle anomalies indicative of various lung diseases as Figure 2 [6][7][8][9][10][11]. ...
... 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(Uçar et al., , 2018c. ...
... With rapidly polluted air, increased pollution, harsh urban life, and consumption of tobacco products such as tobacco, lung disease is one of today's most significant health problems (Melekoğlu et al., 2021;Er et al., 2009;Er, Yumusak & Temurtas, 2012). It has been observed that COPD is the most rapidly increasing but least known chest diseases. ...
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.
... Using vector quantization and neural regression networks, chest diseases has been investigated [4]. In another study [5], neural networks were used to implement the diagnosis of chronic pulmonary disease. Another study [6] utilized radiographic images of the chest to diagnose lung disorders. ...
... 7 Efforts have been made to develop systems supporting physicians in the diagnosis of pneumonia. [8][9][10][11][12][13] However, knowledge of pneumonia diagnosis that these systems rely on is rudimentary and often sufficient for a proof-of-concept demonstration rather than use at the point of care. In recent years, codification of medical knowledge with a help of ontologies gained ground due to their ability to represent medical concepts and relationships among them in a structured and formal manner. ...
Background: Pneumonia is difficult to differentiate from other pulmonary diseases because it shares many symptoms with these diseases. Diagnosing pneumonia in clinical practice would benefit from having access to a codified representation of clinical knowledge. An ontology represents a well-established paradigm for such codification. Objectives: The goal of this research is to create Pneumonia Diagnosis Ontology (PNADO) that brings together the medical knowledge dispersed among multiple medical knowledge sources. Material and Methods: We used several clinical practice guidelines (CPGs) describing the pneumonia diagnostic process as a starting point in developing PNADO. Preliminary version of PNADO was subsequently expanded to cover a broader range of the concepts by reusing ontologies from Open Biological and Biomedical Ontology (OBO) Foundry and BioPortal. PNADO was evaluated by examining relevant concepts from the pneumonia-specific systematic reviews, using patient data from the MIMIC-III clinical dataset, and by clinical domain experts. Results: PNADO is a comprehensive ontology and has a rich set of classes and properties that cover different types of pneumonia, pathogens, symptoms, clinical signs, laboratory tests and imaging, clinical findings, complications, and diagnoses. Conclusion: PNADO unifies pneumonia diagnostic concepts from multiple knowledge sources. It is available in the BioPortal repository.
... In [9], multilayer, probabilistic, learning vector quantization, and generalized regression neural networks have been used for diagnosis chest diseases. The diagnosis of chronic obstructive pulmonary and pneumonia diseases was implemented using neural networks and artificial immune system [10]. In [11], the detection of lung diseases such as TB, pneumonia, and lung cancer using chest radiographs is considered. ...
... However, the pattern layer can be quite huge if the distinction between categories is varied and at the same time quite similar in special areas [5]. Because the PNN provides a general solution to pattern classification problems, it is suitable for the disease diagnosis systems [6][7][8][9][10]. ...
... The classes learned by the competitive layer can be referred as subclasses and the classes of the linear output layer can be referred as target classes [20][21]. The LVQ network structures have been successfully used for the disease diagnosis systems [6][7][8][9][10]. ...
... Computer simulation shows that the PNN-based disease diagnosis system could be very effective in processing the diagnosis information. Certainly our previous works [6][7][8][9][10] show that good performance was achieved by PNN with regards to other disease diagnosis problems [10,[22][23]. Therefore in this paper, a study of PNN on breast tissue cancer diagnosis was realized. ...
Breast cancer is a malignant tumor that has developed from cells of the
breast. Breast cancer is one of the most fatal diseases in the world and a
relatively common cancer in Turkey. Breast cancer diagnosis has been
approached by various machine learning techniques for many years. In this
study, two different probabilistic neural network (PNN) structures were used
for breast cancer’s diagnosis. The PNN results were compared with the
results of the multilayer, learning vector quantization neural networks and the
results of the previous reported studies focusing on breast cancer’s diagnosis
and using the same dataset. It was observed that the PNN is the best
classification accuracy with 98.10% accuracy obtained via 3-fold cross
validation. The present paper describes how this technique can be applied to
the breast tissue classification and the breast cancer detection for medical
devices. The purpose of this study is the classification of the variability of
impedivity observed in normal and pathological breast tissue.
... [26] Despite the urgent need for better VAP diagnostics and the popularity of ML applications in healthcare, relatively little effort has been devoted to the application of ML to EHR data for the purpose of predicting VAP. Several methods have been developed for identifying community-acquired pneumonia using neural networks and genetic algorithms [37][38][39] and one study predicted hospital-acquired pneumonia in patients with schizophrenia. [40] Concerning VAP, studies have examined the accuracy of electronic nose (e-nose) sniffers for screening potential VAP cases. ...
Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay.
A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values.
The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment.
Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.
... 26 They obtained an accuracy result (93.92%) using the probabilistic neural network as the best result. 33 , respectively, and sensitivity, specificity, and accuracy of 70% or higher for COPD diagnosis on the estimated spirometry test results. 35 In our study the ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. ...
... These results are in line with those of previous studies.4,25,32,33 Sujatha et al showed that the ANN methods are applicable for the classification of the spirometric pulmonary function data.4 ...
The spirometry is considered a preclinical tool for the evaluation of the respiratory system. The formal lung volumes measurement and health status lung system are made using spirometry. Artificial neural network (ANN) has been introduced in solving complex problems in a large number of different settings, including medical diagnosis support system as predictive power. An objective of this research was intended to investigate the development of a new decision support system (DSS) using ANN modeling approaches and algorithms to predict pulmonary function in people. The spirometry data and general characteristics, anthropometric data, and body composition parameters (N = 130) were obtained from subjects. The classification of pulmonary function was performed by the multi‐layer perceptron (MLP) model. Findings show that the MLP model is capable of classifying respiratory abnormalities in different people. The ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. Also, a DSS tool was created that allows the evaluation and classification of the results of spirometry data. It appears that ANNs are useful in classification pulmonary function.
... In the previous studies, they did diagnosis classification with 2 classes, and one study with 3 classes [25], [29]. Also, most studies, classified subjects as healthy and ill while some of them classified a subject group with two different illnesses. ...
In this study we compared support vector machines (SVM), k-nearest neighbor (k-NN), and Gaussian Bayes (GB) algorithms in classification of respiratory diseases with text and audio data. An electronic stethoscope and its software are used to record patient information and 17930 lung sounds from 1630 subjects. SVM, k-NN and GB algorithms were run on 6 datasets to classify patients into; (1) sick or healthy with text data, (2) sick or healthy with audio MFCC features, (3) sick or healthy with the text data and audio MFCC features, (4) 12 diseases with text data, (5) for 12 disease with audio MFCC features, (6) for 12 disease with the text data and audio MFCC features. Accuracy results in SVM were %75, %88, %64, %73, %63, %70; for k-NN %95, %92, %92, %67, %64, %66; for GB %98, %91, %97, %58, %48, %58 respectively. In 12 class classification of lung diseases, the most accurate algorithm was SVM with text data. In classifying via audio data, k-NN was the most accurate. Using both audio and text data, SVM was the most accurate. When we classify healthy versus sick via text, audio and combined data, GB was always the most accurate with very high accuracy, closely followed by k-NN. We can infer from here that when we have large number of features but limited amount of samples, SVM and k-NN are best in classifying the dataset in more than two classes. However GB is best when it comes to classifying into two classes.
