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Korenevskiy NA, Belozerov VA, Al-Kasasbeh RT, Al-Smadi MM, Krutskikh V, Shalimova E, Al-Jundi M, Rodionova SN, Filist S, Shaqadan A, Maksim I, Al-Habahbeh OM. Using Fuzzy Mathematical Model in the Differential Diagnosis of Pancreatic Lesions Using Ultrasonography and Echographic Texture Analysis. Crit Rev Biomed Eng 2024; 52:1-20. [PMID: 37938181 DOI: 10.1615/critrevbiomedeng.2023049762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Malignant tumors of the pancreas are the fourth leading cause of cancer-related deaths. This is mainly because they are often diagnosed at a late stage. One of the challenges in diagnosing focal lesions in the pancreas is the difficulty in distinguishing them from other conditions due to the unique location and anatomy of the organ, as well as the similarity in their ultrasound characteristics. One of the most sensitive imaging modalities of the pancreas is endoscopic ultrasonography. However, clinicians recognize that EUS is a difficult and highly operator-dependent method, while its results are highly dependent on the experience of the investigator. Hybrid technologies based on artificial intelligence methods can improve the accuracy and objectify the results of endosonographic diagnostics. Endoscopic ultrasonography was performed on 272 patients with focal lesions of the pancreatobiliary zone, who had been treated in the surgical section of the Kursk Regional Clinical Hospital in 2014-2023. The study utilized an Olympus EVIS EXERA II video information endoscopic system, along with an EU-ME1 ultrasound unit equipped with GF UM160 and GF UC140P-AL5 echo endoscopes. Out of the focal formations in the pancreatobiliary zone, pancreatic cancer was detected in 109 patients, accounting for 40.1% of the cases. Additionally, 40 patients (14.7%) were diagnosed with local forms of chronic pancreatitis. The reference sonograms displayed distinguishable focal pancreatic pathologies, leading to the development of hybrid fuzzy mathematical decision-making rules at the South-West State University in Kursk, Russian Federation. This research resulted in the creation of a fuzzy hybrid model for the differential diagnosis of chronic focal pancreatitis and pancreatic cancer. Endoscopic ultrasonography, combined with hybrid fuzzy logic methodology, has made it possible to create a model for differentiating between chronic focal pancreatitis and pancreatic ductal adenocarcinoma. Statistical testing on control samples has shown that the diagnostic model, based on reference endosonograms of the echographic texture of pancreatic focal pathology, has a confidence level of 0.6 for the desired diagnosis. By incorporating additional information about the contours of focal formations obtained through endosonography, the reliability of the diagnosis can be increased to 0.9. This level of reliability is considered acceptable in clinical practice and allows for the use of the developed model, even with data that is not well-structured.
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Affiliation(s)
| | | | | | | | - Vladislav Krutskikh
- Radio Technical Fundamentals Department, National Research University "MPEI," Moscow, Russia
| | - Elena Shalimova
- Radio Technical Fundamentals Department, National Research University "MPEI," Moscow, Russia
| | - Mohammad Al-Jundi
- Department of Endocrinology, Eunice Kennedy Shriver National Institute of Child and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sofia N Rodionova
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan; South-West State University, Kursk, Russia
| | | | - Ashraf Shaqadan
- Department of Mechatronics Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
| | | | - Osama M Al-Habahbeh
- Department of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan
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Korenevskiy NA, Al-Kasasbeh RT, Krikunova EA, Rodionova SN, Shaqdan A, Al-Habahbeh OM, Filist S, Alshamasin MS, Khrisat MS, Ilyash M. Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields. Crit Rev Biomed Eng 2024; 52:1-16. [PMID: 38884210 DOI: 10.1615/critrevbiomedeng.2024053240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The study aims to enhance the standard of medical care for individuals working in the electric power industry who are exposed to industrial frequency electromagnetic fields and other relevant risk factors. This enhancement is sought through the integration of fuzzy mathematical models with contemporary information and intellectual technologies. The study addresses the challenges of forecasting and diagnosing illnesses within a specific demographic characterized by a combination of poorly formalized issues with interconnected conditions. To tackle this complexity, a methodological framework was developed for synthesizing hybrid fuzzy decision rules. This approach combines clinical expertise with artificial intelligence methodologies to promote innovative problem-solving strategies. Additionally, the researchers devised an original method to evaluate the body's protective capacity, which was integrated into these decision rules to enhance the precision and efficacy of medical decision-making processes. The research findings indicate that industrial frequency electromagnetic fields contribute to illnesses of societal significance. Additionally, it highlights that these effects are worsened by other risk factors such as adverse microclimates, noise, vibration, chemical exposure, and psychological stress. Diseases of the neurological, immunological, cardiovascular, genitourinary, respiratory, and digestive systems are caused by these variables in conjunction with unique physical traits. The development of mathematical models in this study makes it possible to detect and diagnose disorders in workers exposed to electromagnetic fields early on, especially those pertaining to the autonomic nervous system and heart rhythm regulation. The results can be used in clinical practice to treat personnel in the electric power industry since expert evaluation and modeling showed high confidence levels in decision-making accuracy.
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Affiliation(s)
| | | | | | - Sofia N Rodionova
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan; South-West State University, Kursk, Russia
| | | | - Osama M Al-Habahbeh
- Department of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan
| | | | - Mahdi Salman Alshamasin
- Department of Mechatronics Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
| | - Mohammad S Khrisat
- Department of Mechatronics Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
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Filist SA, Al-Kasasbeh RT, Shatalova OV, Aikeyeva AA, Al-Habahbeh OM, Alshamasin MS, Alekseevich KN, Khrisat M, Myasnyankin MB, Ilyash M. Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology. Comput Methods Biomech Biomed Engin 2023; 26:1400-1418. [PMID: 36305552 DOI: 10.1080/10255842.2022.2117551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/20/2022] [Accepted: 08/22/2022] [Indexed: 11/03/2022]
Abstract
Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system.
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Affiliation(s)
- Sergey Alekseevich Filist
- Department of Biomedical Engineering, Faculty of Fundamental and Applied Informatics, Southwestern State University, Kursk, Russia
| | - Riad Taha Al-Kasasbeh
- Department of Mechatronics Engineering, School of Engineering, University of Jordan, Amman, Jordan
| | - Olga Vladimirovna Shatalova
- Department of Biomedical Engineering, Faculty of Fundamental and Applied Informatics, Southwestern State University, Kursk, Russia
| | - Altyn Amanzholovna Aikeyeva
- Department of Radio Engineering, Electronics and Telecommunications, Faculty of Physics and Technology, L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan
| | - Osama M Al-Habahbeh
- Mechatronics Engineering Department, The University of Jordan, Amman, Jordan
| | - Mahdi Salman Alshamasin
- Department of Mechatronics Engineering, Al-Balqa Applied University, Faculty of Engineering Faculty, Amman, Jordan
| | - Korenevskiy Nikolay Alekseevich
- Department of Biomedical Engineering, Faculty of Fundamental and Applied Informatics, Southwestern State University, Kursk, Russia
| | - Mohammad Khrisat
- Department of Mechatronics Engineering, School of Engineering, University of Jordan, Amman, Jordan
| | | | - Maksim Ilyash
- National Research University of Information Technologies, Mechanics and Optics (ITMO University), Saint-Petersburg, Russian Federation
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Karuppuchamy V, Palanivelrajan S. Efficient IoT-machine learning assisted heart failure prediction using adaptive fuzzy-based LSTM-RNN algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-224298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Chronic diseases like diabetes, Heart Failure (HF), malignancy, and severe respiratory sickness are the leading cause of mortality around the globe. Dissimilar indications or traits are extremely difficult to identify in HF patients. IoT solutions are becoming increasingly commonplace as smart wearable gadgets become more popular. Sudden heart attacks have a short life expectancy, which is terrible. As a result, a patient monitoring of heart patients based on IoT-centered Machine Learning (ML) is presented to help with HF prediction, and treatment is administered as necessary. Verification, Encryption, and Categorization are the three phases that make up this developed model. Initially, the datasets from the IoT sensor gadget are gathered by authenticating with a specific hospital through encryption. The patient’s integrated IoT sensor module then transfers sensing information to the cloud. The Improved Blowfish Encryption (IBE) approach is used to protect the sensor data transfer to the cloud. Then the encrypted data is decrypted, and the classification is performed using the Adaptive Fuzzy-Based Long Short-Term Memory with Recurrent Neural Network (AF-LSTM-RNN) algorithm. The results are classed as malignant or benign. It assesses the patient’s cardiac state and sends an alert text to the doctor for treatment. The AF-LSTM-RNN-based HF prediction outperforms the existing techniques. Accuracy, sensitivity, specificity, precision, F-measure and Matthews Correlation Coefficient (MCC) are compared to existing procedures to ensure the planned research is genuine. Using the Origin tool, these metrics are shown as research findings.
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Affiliation(s)
- V. Karuppuchamy
- Department of Information Technology, Kongunadu College of Engineering and Technology, Thottiam, Trichy, Tamilnadu, India
| | - S. Palanivelrajan
- Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering (Autonomous), Thalavapalayam, Karur, Tamilnadu, India
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Balakrishnan C, Ambeth Kumar VD. IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13040775. [PMID: 36832263 PMCID: PMC9955174 DOI: 10.3390/diagnostics13040775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/22/2023] Open
Abstract
Cardiovascular diseases currently present a key health concern, contributing to an increase in death rates worldwide. In this phase of increasing mortality rates, healthcare represents a major field of research, and the knowledge acquired from this analysis of health information will assist in the early identification of disease. The retrieval of medical information is becoming increasingly important to make an early diagnosis and provide timely treatment. Medical image segmentation and classification is an emerging field of research in medical image processing. In this research, the data collected from an Internet of Things (IoT)-based device, the health records of patients, and echocardiogram images are considered. The images are pre-processed and segmented, and then further processed using deep learning techniques for classification as well as forecasting the risk of heart disease. Segmentation is attained via fuzzy C-means clustering (FCM) and classification using a pretrained recurrent neural network (PRCNN). Based on the findings, the proposed approach achieves 99.5% accuracy, which is higher than the current state-of-the-art techniques.
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Affiliation(s)
- Chitra Balakrishnan
- Panimalar Engineering College, Anna University, Chennai 600123, India
- Correspondence: (C.B.); (V.D.A.K.)
| | - V. D. Ambeth Kumar
- Computer Engineering, Mizoram University, Aizawl 796004, India
- Correspondence: (C.B.); (V.D.A.K.)
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Korenevskiy NA, Bykov AV, Al-Kasasbeh RT, Al-Smadi MM, Aikeyeva AA, Al-Jund M, Al-Kasasbeh ET, Rodionova SN, Ilyash M, Shaqadan A. Development of a Fuzzy Diagnostic Model of Ischemic Disease of the Lower Limbs for Different Stages of Patient Management. Crit Rev Biomed Eng 2023; 50:13-30. [PMID: 36734864 DOI: 10.1615/critrevbiomedeng.2022044974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Ischemic disease has severe impact on patients which makes accurate diagnosis vital for health protection. Improving the quality of prediction of patients with ischemic extremity disease by using hybrid fuzzy model allows for early and accurate prognosis of the development of the disease at various stages. The prediction of critical ischemia of lower extremity (CLI) at various disease stages is complex problem due to inter-related factors. We developed hybrid fuzzy decision rules to classify ischemic severity using clinical thinking (natural intelligence) with artificial intelligence, which allows achieving a new quality in solving complex systemic problems and is innovative. In this study mathematical model was developed to classify the risk level of CLI into: subcritical ischemia, favorable outcome, questionable outcome, and unfavorable outcome. The prognosis is made using such complex indicators as confidence that the patient will develop gangrene of the lower extremity (unfavorable outcome), complex coefficient of variability, and reversibility of the ischemic process. Model accuracy was calculated using representative control samples that showed high diagnostic accuracy and specificity characterizing the quality of prediction are 0.9 and higher, which makes it possible to recommend their use in medical practice.
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Affiliation(s)
| | | | - Riad Taha Al-Kasasbeh
- Department of Mechatronics Engineering, School of Engineering, University of Jordan, Amman, Jordan
| | | | - Altyn A Aikeyeva
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan
| | - Mohammad Al-Jund
- Department of Endocrinology, Eunice Kennedy Shriver National Institute of Child and Human Development, National Institutes of Health, Bethesda, MD, USA
| | | | - Sofia N Rodionova
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan
| | - Maksim Ilyash
- ITMO University Kronverksky, St. Petersburg 197101, Russia; Zarqa University, Civil Engineering Department, Jordan
| | - Ashraf Shaqadan
- ITMO University Kronverksky Pr. 49, Bldg. A, St. Petersburg, 197101, Russia
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Korenevskiy NA, Belozerov VA, Al-Kasasbeh RT, Al-Smadi MM, Aikeyeva AA, Al-Jundi M, Rodionova SN, Filist S, Alshamasin MS, Al-Habahbeh OM, Maksim I. Differential Diagnosis of Pancreatic Cancer and Chronic Pancreatitis According to Endoscopic Ultrasonography Based on the Analysis of the Nature of the Contours of Focal Formations Based on Fuzzy Mathematical Models. Crit Rev Biomed Eng 2023; 51:59-76. [PMID: 37560879 DOI: 10.1615/critrevbiomedeng.2023048046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
One of the key echographic signs of focal pathology of the pancreas is the presence of formation contours and their nature. Endoscopic ultrasonography has a unique ability to visualize the echographic texture of the pancreatic parenchyma, and also allows you to assess in detail the boundaries and nature of the contours of the tumor formations of the organ due to the proximity of the ultrasound sensor. However, the differential diagnosis of focal pancreatic lesions remains a difficult clinical task due to the similarity of their echosemiotics. One of the ways to objectify and improve the accuracy of ultrasound data is the use of artificial intelligence methods for interpreting images. Improving the quality of differential diagnosis of focal pathology of the pancreas according to endoscopic ultrasonography based on the analysis of the nature of the contours of focal formations using fuzzy mathematical models.
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Affiliation(s)
| | | | - Riad Taha Al-Kasasbeh
- Department of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan
| | | | - Altyn A Aikeyeva
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan
| | - Mohammad Al-Jundi
- Department of Endocrinology, Eunice Kennedy Shriver National Institute of Child and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sofia N Rodionova
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan; South-West State University, Kursk, Russia
| | | | - Mahdi Salman Alshamasin
- Department of Mechatronics Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
| | - Osama M Al-Habahbeh
- Department of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan
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Korenevskiy NA, Al-Kasasbeh RT, Al-Kasasbeh ET, Al-Smadi MM, Aikeyeva AA, Al-Jundi M, Rodionova SN, Al-Habahbeh OM, Filist S, Alshamasin MS, Maksim I. Method for Determining the Body's Level of Protection According to Oxidant Status in Assessing the Influence of Industrial Risk Factors on Health. Crit Rev Biomed Eng 2023; 51:1-17. [PMID: 37551905 DOI: 10.1615/critrevbiomedeng.2023047224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
This work aims at improving the quality of health assessments, specifically under the influence of occupational risk factors. For this purpose, additional informative indicators are utilized in prognostic and diagnostic models. The models are used to characterize the level of body protection based on oxidative status. A quantitative method is proposed to assess the body's level of protection by means of the levels of lipid peroxidation and antioxidant activity, which characterize the body's oxidative status. A mechanism is developed for integrating the proposed method into prognostic and diagnostic decision rules. The developed rules are in the form of mathematical models used to synthesize hybrid fuzzy decision rules, which are then used to quantify the level of body protection (LBP) against external risk factors, based on the use of protection level functions in terms of lipid peroxidation and antioxidant activity. A mechanism for embedding LBP into predictive and diagnostic decision rules has been proposed. The proposed method is used to predict the occurrence and development of coronary heart disease in railroad locomotive drivers. It was found that to improve the predicting and diagnosing of diseases caused by external pathogenic factors, quantitative assessments of LBP, determined by oxidative status, can be implemented. It has been established that the use of the protection level indicator in predictive decision rules makes it possible to increase the efficiency of the prediction while simultaneously increasing its accuracy.
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Affiliation(s)
| | - Riad Taha Al-Kasasbeh
- Department of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan
| | | | | | - Altyn A Aikeyeva
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan
| | - Mohammad Al-Jundi
- Department of Endocrinology, Eunice Kennedy Shriver National Institute of Child and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sofia N Rodionova
- Eurasian National University named after L.N. Gumilyov, Nur-Sultan, Kazakhstan; South-West State University, Kursk, Russia
| | - Osama M Al-Habahbeh
- Department of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan
| | | | - Mahdi Salman Alshamasin
- Department of Mechatronics Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
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Khatatneh K, Filist S, Al-Kasasbeh RT, Aikeyeva AA, Namazov M, Shatalova O, Shaqadan A, Miroshnikov A. Hybrid neural networks with virtual flows in in medical risk classifiers. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212617] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Modern medical risk classification systems focus on traditional risk factors and modeling methods. The available modeling tools do not allow reliable prediction of the of disease severity. In this study we develop prediction model of recurrent myocardial infarction in the rehabilitation period using several health variables generated in virtual flows. Hybrid decision modules with health data flows were used to build prognostic model for the prediction of disease. The vector of input information features consists of two subvectors: the first reflects real flows, the second reflects virtual flows. Complex interrelations among input data are modelled using Neural Network structure. The model classification quality of the intellectual cardiovascular catastrophe prediction system was tested on a sample composed of 230 patients who had acute myocardial infarction. For prediction, three categories of risk factors were identified: traditional factors, factors associated with stressful overloads, and risk factors derived from bio-impedance studies. During the rehabilitation period, the level of molecular products of lipid peroxidation and the antioxidant potential of blood serum were also studied. Experimental studies of various modifications of the proposed classifier model were conducted, consisting of sequential disconnection from the aggregator of solutions of “weak” classifiers at various hierarchical levels. The mathematical model show predictions accuracy of correct prognosis for the risk of myocardial infarction exceeding 0.86. Prediction quality indicators are higher than the known ASCORE cardiovascular catastrophe prediction system, on average, by 14%.
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Affiliation(s)
- Khalaf Khatatneh
- Department of Computer, Balqa Applied University, Prince Abdullah bin Ghazi faculty for Communication and Information Technology
| | - Sergey Filist
- Department of Biomedical Engineering, Southwest State University, Kursk
| | | | | | | | - Olga Shatalova
- Department of Biomedical Engineering, Southwest State University, Kursk
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Filist S, Al-Kasasbeh RT, Shatalova O, Korenevskiy N, Shaqadan A, Protasova Z, Ilyash M, Lukashov M. Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points. JOURNAL OF INTEGRATIVE MEDICINE 2022; 20:252-264. [PMID: 35288062 DOI: 10.1016/j.joim.2022.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
Abstract
OBJECTIVE This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery. METHODS A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia. RESULTS Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%. CONCLUSION The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.
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Affiliation(s)
- Sergey Filist
- Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation
| | | | - Olga Shatalova
- Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation
| | - Nikolay Korenevskiy
- Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation
| | - Ashraf Shaqadan
- Civil Engineering Department, Zarqa University, Zarqa Governorate 13222, Jordan
| | - Zeinab Protasova
- Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation
| | - Maksim Ilyash
- Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint-Petersburg 197101, Russian Federation
| | - Mikhail Lukashov
- Pediatric Faculty, Kursk State Medical University, Kursk 305041, Russian Federation
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Korenevskiy NA, Al-Kasasbeh RT, Shaqadan A, Eltous Y, Alshamasin MS, Myasoedova MA, Rodionova SN, Ilyash M. Prediction of Occupational Diseases Due to Exposure to High Radiation Electromagnetic Environment Using a Fuzzy Logic Model. Crit Rev Biomed Eng 2021; 49:41-55. [PMID: 35993950 DOI: 10.1615/critrevbiomedeng.2022043586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Several researchers studied the health impacts of electromagnetic fields in work environment. However, the previous research focuses on the statistical analysis of past exposure. There are no studies that addressed prediction of health symptoms. Prediction and early diagnosis of occupational diseases of electric power workers with acceptable accuracy is needed. The objective of this study is to develop a data driven mathematical model for predicting and diagnosis of occupational diseases in workers in electric power industry. The complex nature of disease occurrence due to electromagnetic radiation is appropriate for the fuzzy rules set by medical experts which are analyzed and validated to produce hybrid fuzzy decision rules. The selected group of medical experts suggested using hormonal disorders, endocrine diseases, coffee abuse, chronic diseases of the internal organs, allergic diseases, cervical osteochondrosis, severe course of infectious diseases, intoxication, injury. The developed hybrid fuzzy logic model predicts high risk of developing nervous system diseases. The prediction accuracy exceeded 0.88, which is acceptable for supporting tool.
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Affiliation(s)
| | - Riad Taha Al-Kasasbeh
- Department of Electrical Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
| | | | - Yousif Eltous
- Department of Electrical Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
| | - Mahdi Salman Alshamasin
- Mechatronics Department, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
| | | | | | - Maksim Ilyash
- ITMO University Kronverksky, St. Petersburg 197101, Russia
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