Computers and Electronics in Medicine
type: channel
description: Computers and Electronics in Medicine (CEM) is a peer-reviewed interdisciplinary journal dedicated to advancing the field of medical technology through the integration of computer science, electronics, and healthcare. The journal serves as a premier platform for researchers, engineers, clinicians, and practitioners to share and disseminate cutting-edge research findings, innovative technologies, and practical applications at the intersection of computing and medicine. The journal publishes regular papers dealing with the following areas, but not limited to: - Medical Imaging - Bioinformatics and Health Informatics - Wearable Devices and Remote Monitoring - Telemedicine and Telehealth - Medical Robotics and Automation - Artificial Intelligence in Medicine - Biomedical Signal Processing - Mathematical Modeling and Simulation - Health Data Security and Privacy - Human-Computer Interaction in Medicine - Biomedical Engineering and Devices - Complex Systems and Network Analysis Computers and Electronics in Medicine emphasizes the importance of interdisciplinary collaboration to address complex healthcare challenges. The journal encourages submissions that bridge the gap between theoretical research and practical applications, fostering innovations that can be translated into clinical practice. By providing a comprehensive and inclusive platform, the journal aims to drive advancements that enhance patient care, improve diagnostic and therapeutic techniques, and contribute to the overall betterment of global health outcomes. The journal also supports the exploration of emerging technologies and methodologies that have the potential to revolutionize healthcare, such as blockchain for secure health data management, augmented reality (AR) and virtual reality (VR) for medical training and simulation, and the use of 3D printing in personalized medicine and prosthetics. Computers and Electronics in Medicine is committed to publishing high-impact research that advances the integra…
publisher: adbascientific
language: en
publication name: Computers and Electronics in Medicine
issn: 3023-8609
copyright: The following copyright agreement is read and approved by authors before they publish their work to our journal's online system. The copyright notice text is regarded to have been approved by them after they give their approval. Copyright Notice Text Author(s); a) The submitted article is the original work of the author(s), and they do not plagiarize, b) All authors have participated in this study individually, and they take all responsibility for the study. c) All authors have seen and approved the final version of the submitted article, d) The article has not been published elsewhere or submitted for publication, e) They accept and undertake that the text, figures, and documents in the article do not violate the copyrights of other parties. The authors are deemed to have accepted the following terms by submitting their work to the journal under the CC BY-NC license. The copyright of the published article belongs to its author under CC BY-NC 4.0 license. The authors authorize the journal to publish the article and identify itself as the original publisher. The authors grant the right to freely use the article to any third party as long as the original authors and citation details are identified. Nothing in this license impairs or restricts an author's right to preserve the integrity and ownership of their work. All commercial rights related to the article belong to the authors. The authors own the copyright of their work published in CEM, and their work is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license allows the work to be shared, copied, and reproduced in any size and format except for commercial use. It also allows it to be rearranged, transformed, and adapted, including building upon the original work, with proper reference to it. CC BY-NC 4.0: https://creativecommons.org/licenses/by-nc/4.0/ The license owner cannot cancel these r…
items: A Chaos-Based Encryption Scheme for Secure Medical X-ray Images
items: Deep Learning in Robot-Assisted Surgery: A Conceptual Framework for the da Vinci System
items: AI-Driven Classification of Anemia and Blood Disorders Using Machine Learning Models
items: A Modified Transmission Line Modelling Approach for Bioheat Transfer
items: Deep Learning-Based Detection of Abdominal Diseases Using YOLOv9 Models and Advanced Preprocessing Techniques
items: Investigation of the Effect of Alloying Elements on the Density of Titanium-Based Biomedical Materials Using Explainable Artificial Intelligence
items: Bibliometric Analysis of Publications on Clinical Studies Leveraging Natural Language Processing During 2000 - 2023
items: Collaborative Care: Multi-Agent Systems in Healthcare
items: ANN Algorithms for Parkinson's, ALS, Huntington, and Healthy Walking Detection
items: Machine Learning Interpretability in Diabetes Risk Assessment: A SHAP Analysis
items: Bibliometric Analysis of Publications on Action Recognition, Convolutional Neural Network, Video Surveillance During 2011-2021
items: Juxtaposition across Mathematical Modeling, Stochastic Processes along with the Lenses of Philosophy of Science and AI integration in Medicine and Biology: An Overview
items: Examination and Evaluation of Obesity Risk Factors with Explainable Artificial Intelligence

A Chaos-Based Encryption Scheme for Secure Medical X-ray Images
type: item
description: Data privacy in healthcare system is getting more importance day by day. In this study we introduce a novel chaos-based encryption scheme for medical X-ray images. We used a double Lorenz map as chaotic system to produce random key streams in confusion phase and in diffusion phase. We also used hash value of the original file to determine initial parameters of second chaotic map. So, our scheme is highly resistant to differential attacks. In confusion phase, we employed row-based and column-based shift operation to confuse original data. In diffusion phase we used XoR operation to encrypt data. Since Lorenz map has 3 dimensions, we used each one for different purposes like X dimension is for row-based shift operation, Y dimension is for column-based shift operation, Z dimension is for data distribution, XoR operation etc. On average, our scheme generates a NPCR value exceeding 99.66% and an UACI value of approximately 33.39% when there is a one-pixel alteration in the plaintext. Additionally, it yields an average information entropy value greater than 7.9976. Since our method utilizes a double Lorenz map, it is resilient against brute force attacks. The results from our tests and analyses indicate that our schema is pretty fast, dependable, resilient, practical, and effective. It serves as a solid encryption scheme option for medical images.
subject: Articles
  topic: section
subject: Nonlinear dynamics
  topic: keywords
subject: Chaos
  topic: keywords
subject: Medical image encryption
  topic: keywords
subject: Hash-Code
  topic: keywords
subject: Security
  topic: keywords
creator: Yasin Kaya
creator: Zeynep Gürkas Aydin
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 53
ending page: 59
doi: 10.69882/adba.cem.2025074

Deep Learning in Robot-Assisted Surgery: A Conceptual Framework for the da Vinci System
type: item
description: This study proposes a conceptual framework for integrating deep learning into the da Vinci Surgical System. The framework was developed after identifying common applications and challenges through a systematic review. It combines multiple data types, including visual, kinematic, and physiological signals, into a closed-loop system. This system includes four core components: data acquisition and fusion, deep learning-based analysis, adaptive control and feedback, and continuous skill assessment. These components interact to support real-time surgical guidance and personalized training, aiming to improve surgical outcomes. To inform the framework, a systematic search of the PubMed database was conducted, focusing on studies that combine deep learning with the da Vinci system. Two major application areas were identified: the first involves autonomous or semi-autonomous instrument control and image-guided navigation, and the second covers surgical skill assessment and workflow analysis. While existing studies demonstrate the potential of artificial intelligence in robotic surgery, they also reveal technical and practical limitations. By analyzing these common approaches and obstacles, this study provides a structured foundation for future research. The proposed framework offers a unified view that connects data processing, intelligent decision-making, and feedback, paving the way for smarter systems that assist both in real-time procedures and long-term training, and helping bridge the gap between advanced robotic hardware and cognitive support in surgical environments.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Robotic surgery
  topic: keywords
subject: Robot-assisted surgery
  topic: keywords
subject: da Vinci System
  topic: keywords
subject: Conceptual framework
  topic: keywords
subject: Decision support system
  topic: keywords
creator: Hakan Özcan
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 26
ending page: 35
doi: 10.69882/adba.cem.2025071

AI-Driven Classification of Anemia and Blood Disorders Using Machine Learning Models
type: item
description: Anemia and other blood disorders are serious global health issues affecting millions of individuals. These conditions, often triggered by insufficient hemoglobin or red blood cells, can manifest through symptoms like fatigue, weakness, and reduced immune function. When such disorders progress into advanced stages, they can compromise organ function and overall quality of life making early diagnosis especially critical. In recent years, as the value of prompt detection has become increasingly clear, artificial intelligence (AI) and autonomous diagnostic technologies have begun to take center stage in the medical community. Machine learning models excel at parsing complex datasets and generating accurate, rapid assessments, thus offering clinicians robust decision-support tools. Through these AI-driven methods, healthcare professionals can better interpret patients’ blood metrics and clinical indicators, enabling them to identify diseases at earlier stages and develop more effective treatment strategies. This study proposes a machine learning–based approach to classify various types of anemia and related blood disorders, including iron deficiency anemia, leukemia, and thrombocytopenia. We trained five contemporary algorithms Decision Tree (DT), Random Forest (RF), CatBoost, Gradient Boosting (GB), and XGBoost using critical blood parameters such as white and red blood cell counts, hemoglobin levels, and platelet counts. Notably, Gradient Boosting emerged as the most accurate model, achieving an impressive 99.19% accuracy rate. These findings underscore how AI-powered autonomous diagnostic systems have the potential to revolutionize hematology by facilitating earlier and more precise disease detection.
subject: Articles
  topic: section
subject: Anemia
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Blood disorders
  topic: keywords
subject: Clinical decision support
  topic: keywords
subject: Hematological data
  topic: keywords
creator: Yiğitcan Cakmak
creator: Ishak Pacal
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 43
ending page: 52
doi: 10.69882/adba.cem.2025073

A Modified Transmission Line Modelling Approach for Bioheat Transfer
type: item
description: This study presents a modified Transmission Line Modelling (TLM) method for simulating bioheat transfer in biological tissues, with a particular focus on accounting for non-unidirectional blood flow. While the Pennes bioheat equation has been widely used for such problems, it assumes unidirectional perfusion and may overlook important thermal effects introduced by non-unidirectional blood flow. To address this limitation, a TLM-based formulation of the Klinger bioheat equation known for incorporating flow directionality is developed and implemented for one-dimensional (1D) problems. A novel TLM cell model is introduced to represent the governing equation and its associated boundary conditions. Validation is performed through a 1D multilayer human tissue model exposed to electromagnetic (EM) fields at multiple 4G carrier frequencies. Comparative results with the Finite Element Method (FEM) show strong agreement, especially under steady-state conditions. Furthermore, the proposed model reveals that the impact of non-unidirectional blood flow becomes more pronounced in thicker tissue layers, confirming the importance of incorporating convective terms. This work demonstrates that the modified TLM approach provides a stable, efficient, and more physiologically accurate method for bioheat simulations.
subject: Articles
  topic: section
subject: TLM
  topic: keywords
subject: Bioheat transfer
  topic: keywords
subject: Klinger bioheat equation
  topic: keywords
subject: Pennes bioheat equation
  topic: keywords
creator: Burak Arıcıoğlu
creator: Abdullah Ferikoğlu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-30
publication date: 2025-07-30
starting page: 36
ending page: 42
doi: 10.69882/adba.cem.2025072

Deep Learning-Based Detection of Abdominal Diseases Using YOLOv9 Models and Advanced Preprocessing Techniques
type: item
description: Artificial intelligence has emerged as a transformative tool in medical imaging, enabling automated diagnosis and analysis across various domains. While significant advancements have been made in abdominal imaging, many studies struggle to achieve robust detection of diseases. The complexity and variability in abdominal structures present unique challenges for traditional machine learning models, necessitating the adoption of more advanced object detection frameworks. Motivated by these challenges, this study focuses on leveraging the YOLOv9 object detection architecture to enhance the identification of abdominal diseases using the TEKNOFEST 2022 Abdomen Dataset. Advanced preprocessing techniques, including CLAHE (Contrast Limited Adaptive Histogram Equalization) and Gaussian noise augmentation, were applied to improve image contrast and model robustness. The dataset was processed into YOLO-compatible formats, and multiple training configurations were evaluated using YOLOv9c and YOLOv9s variants. These configurations included variations in batch size, optimizer type (SGD and Adam), dropout rate, and frozen layers. Among the configurations tested, the YOLOv9s model with 32 batch size, SGD optimizer, and a 35% dropout rate demonstrated the best performance, achieving a Recall of 0.7698, Accuracy of 0.7698, and F1 Score of 0.8228. The highest mAP50 of 0.9385 was observed with the YOLOv9c model trained using the Adam optimizer and a 35% dropout rate. Confusion matrix analysis revealed strong detection capabilities for conditions like acute cholecystitis and abdominal aortic aneurysm. This study highlights the potential of YOLOv9 models in medical imaging and emphasizes the importance of high-resolution datasets and advanced feature extraction techniques for improving diagnostic accuracy in abdominal disease detection. These findings lay a foundation for the development of reliable and efficient AI-driven diagnostic tools.
subject: Articles
  topic: section
subject: Abdominal disease detection
  topic: keywords
subject: YOLOv9
  topic: keywords
subject: Medical image analysis
  topic: keywords
subject: Preprocessing techniques
  topic: keywords
subject: Deep learning for diagnosis
  topic: keywords
creator: Harun Emre Kıran
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 20
ending page: 25
doi: 10.69882/adba.cem.2025014

Investigation of the Effect of Alloying Elements on the Density of Titanium-Based Biomedical Materials Using Explainable Artificial Intelligence
type: item
description: Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.
subject: Articles
  topic: section
subject: Explainable artificial intelligence
  topic: keywords
subject: Computational materials science
  topic: keywords
subject: Biomedical materials
  topic: keywords
subject: Titanium alloys
  topic: keywords
creator: Yusuf Alaca
creator: Yusuf Uzunoğlu
creator: Berkay Emin
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 15
ending page: 19
doi: 10.69882/adba.cem.2025013

Bibliometric Analysis of Publications on Clinical Studies Leveraging Natural Language Processing During 2000 - 2023
type: item
description: The number of clinical studies using natural language processing is quite large. Therefore, it is important to examine in depth the development of clinical studies using Natural Language Processing over the years. However, there are a limited number of studies in the literature examining the research status of this field. The article presents a bibliometric analysis of studies on the keywords "clinical AND studies AND natural AND language AND processing" indexed in Scopus between 2000 and 2023. This study aims to evaluate academic outputs in the relevant field quantitatively, make sense of the data, reveal the state of scientific knowledge in the field, and give scientists a general perspective on the subject. Bibliometrix and Microsoft Excel programs were used for bibliometric analysis. Nineteen thousand two hundred seventy-three different authors identified a total of 4535 studies. 77.5% of these studies were research articles (3516), 14.8% were conference papers (669), 6.8% were reviews (307), and 0.9% were book chapters (43). Journal of Biomedical Informatics was the journal in which the most studies were published, with 226 articles. Only the United States (2637) contributed 58.1% to the studies. Liu, H. was the most prolific author, with 85 articles. Harvard Medical School was the most productive institution, with 304 studies. The most cited article was Discontinuation of Statins in Routine Care Settings, A cohort study.
subject: Articles
  topic: section
subject: Clinical studies
  topic: keywords
subject: Natural language processing
  topic: keywords
subject: Bibliometric analysis
  topic: keywords
subject: Citation analysis
  topic: keywords
subject: Network analysis
  topic: keywords
creator: Ömer Faruk Akmeşe
creator: Buğra Bağcı
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
date: 2025-01-31
publication date: 2025-01-31
starting page: 6
ending page: 14
doi: 10.69882/adba.cem.2025012

Collaborative Care: Multi-Agent Systems in Healthcare
type: item
description: Everyday thousands of IoT devices are added to networks globally. Medical IoT (IoMT) is a subdomain, which has had much success over recent years and is expanding rapidly. However, interoperability is poor with IoMT devices, and even the sharing of basic patient data is generally poor between healthcare systems. There is considerable waste and unnecessary duplication of data across devices, which grows exponentially across healthcare systems. Multi-agent systems have the potential to reduce waste and expenditure while improving efficiency and personalizing patient care and may offer the potential to realize both organizational and patient care benefits.
subject: Articles
  topic: section
subject: Healthcare
  topic: keywords
subject: Collaborative care
  topic: keywords
subject: IoT
  topic: keywords
subject: IoMT
  topic: keywords
creator: David Power
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-31
publication date: 2025-01-31
starting page: 1
ending page: 5
doi: 10.69882/adba.cem.2025011

ANN Algorithms for Parkinson's, ALS, Huntington, and Healthy Walking Detection
type: item
description: In this study, the utilization of artificial neural networks (ANN) algorithms, in the diagnosis of neurodegenerative diseases were examined. Data obtained from the measurement of walking parameters were evaluated for disease diagnosis using the ANN model among individuals with ALS, Parkinson's, Huntington's, and healthy individuals. Comparative analyses conducted using Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms demonstrate that the Levenberg-Marquardt algorithm provides the most effective diagnosis with a success rate of 99%. This study highlights the potential of artificial neural networks in the early diagnosis of neurodegenerative diseases and lays a foundation for future research. In conclusion, artificial neural networks may play a significant role in the diagnosis of neurodegenerative diseases, but further research and method development in this area are warranted.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: Neurodegenerative diseases
  topic: keywords
subject: Disease diagnosis
  topic: keywords
subject: Parkinson’s ALS
  topic: keywords
subject: Huntington
  topic: keywords
creator: Fatma Betül Derdiyok
creator: Kasım Serbest
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 18
ending page: 23
doi: 10.69882/adba.cem.2024073

Machine Learning Interpretability in Diabetes Risk Assessment: A SHAP Analysis
type: item
description: Diabetes continues to be a complicated and prevalent metabolic illness, providing a serious burden to public health. While machine learning approaches like extreme gradient boosting (XGBoost) provide intriguing options for diabetes prediction, their 'black-box' nature typically limits clinical interpretability. To overcome this gap, our work applied SHapley Additive exPlanations (SHAP) to give insights into the XGBoost model's predictions. The dataset utilized in this research comprised of 253,680 patients and contained 21 parameters, such as General Health Status, High Blood Pressure Status, Age, and Body Mass Index. After feature selection using Recursive Feature Elimination (RFE), 15 important characteristics were discovered. In the test set, the XGBoost model obtained an accuracy of 86.6%, precision of 54.1%, recall of 17.0%, and an F1-score of 25.9% for the Original dataset. For the RFE dataset, the model displayed an accuracy of 86.6%, precision of 54.9%, recall of 16.5%, and an F1-score of 25.3%. SHAP analysis found that General Health Status, High Blood Pressure Status, Age, and Body Mass Index were the most important characteristics in both the Original and RFE datasets. This work provides as a platform for transparent and clinically applicable predictive modeling, assisting in early diabetes identification and preventive healthcare.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Diabetes
  topic: keywords
subject: Recursive feature elimination
  topic: keywords
subject: Engineering
  topic: disciplines
subject: Artificial Intelligence
  topic: disciplines
creator: Mustafa Kutlu
creator: Turker Berk Donmez
creator: Chris Freeman
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 34
ending page: 44
doi: 10.69882/adba.cem.2024075

Bibliometric Analysis of Publications on Action Recognition, Convolutional Neural Network, Video Surveillance During 2011-2021
type: item
description: Action recognition based on convolutional neural networks (AR-CNN) has been developing rapidly in recent years. It is of great significance to conduct a deep analysis to understand the recent development of AR-CNN. However, a limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the publications related to the SciVal topic "Action Recognition; Convolutional Neural Network; Video Surveillance (T.561)" in computer vision research. This study focused on six aspects: literature distribution characteristics analysis, the development trend, citation analysis, collaborative analysis, keyword analysis and thematic evolution, using VOSviewer and Bibliometrix. The relevant publications were retrieved from Scopus in the period 2012–2021. A total of 6633 publications were identified by 9088 different authors, 62% were conference papers, and 35% were research articles. China and the USA contributed 39.7 % and 17.9 % of the total publications, respectively. The authors' production according to Price's Law was inconsistent but consistent according to Lotka's Law. Ling Shao was the most productive author, with 48 papers (%0.7). Chinese Academy of Sciences was the most productive affiliation, with 259 papers (%3.9). The first Bradford site consisted of Computer Science Lecture Notes with 617 publications. A moderately significant correlation was revealed between the country's publications and GDP per capita. The overall results show that the number of AR-CNN-related documents has increased significantly in recent years, with rapid growth from 2016. Although publications on AR-CNN were published mainly in European journals, China led the scientific production.
subject: Articles
  topic: section
subject: Action recognition
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Convolutional neural network
  topic: keywords
subject: Video surveillance
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Bibliometric analysis
  topic: keywords
creator: Meryem Yalçınkaya
creator: Ömer Faruk Akmeşe
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 24
ending page: 33
doi: 10.69882/adba.cem.2024074

Juxtaposition across Mathematical Modeling, Stochastic Processes along with the Lenses of Philosophy of Science and AI integration in Medicine and Biology: An Overview
type: item
description: The ultimate reason for the ubiquity of mathematics in modern science entails the essence of mathematical thinking and processes so that complex phenomena including those emerging in medical and biological systems can be understood, and thus, scientific models at their crux can be generated. The consequent complexities and uncertainties require the applications of stochastic processes in mathematical modeling with Artificial Intelligence (AI) techniques used in realms of medicine and biology. Within these conditions, clinical evaluation evidence and model explainability are considered to ensure accountable, effective and safe uses of AI in clinical settings, along with robust, reliable as well as accurate understanding of various complex processes that manifest huge numbers of heterogeneous temporospatial scales. The role of philosophy within science can be expounded by its juxtaposition with models and empirical data explicated by philosophy whose pillars are driven into semantic, pragmatic and syntactic structures of scientific theory that also make up the foundational tenets of algorithmic thinking and patterns. While philosophy of science examines and reflects on the concepts, theories, arguments and methods of science, it should also be borne in mind that scientific theory, by its definition, relates to applications validated by its predictions as units of analyses. Concerning mathematical models, their structure and behavior in target systems are also to be addressed so that explicit common patterns that are implicit in scientific practice can be included in this complex influx. On the other hand, critical functions of mathematical modeling from the pragmatic aspect include the unification of models and data, model fitting to the data, identification of mechanisms depending on observations as well as predictions of future observations. Given these, philosophy of science in medical and biological fields is stated to prompt a comprehensive understanding to const…
subject: Articles
  topic: section
subject: Mathematical Modeling
  topic: keywords
subject: AI-based mathematical modeling
  topic: keywords
subject: Differential equations
  topic: keywords
subject: Philosophy of science
  topic: keywords
subject: Fractional calculus
  topic: keywords
subject: Fractals
  topic: keywords
subject: Applied Mathematics
  topic: keywords
subject: Computational fractalbased methodologies in medicine and biology
  topic: keywords
subject: Engineering applications in medicine and biology
  topic: keywords
subject: Biomedical decisionmaking
  topic: keywords
subject: Probabilistic clinical reasoning
  topic: keywords
subject: Stochastic processes
  topic: keywords
subject: Brownian motion
  topic: keywords
subject: Data analytics-based models
  topic: keywords
subject: Data distribution
  topic: keywords
subject: Dynamic precision medicine
  topic: keywords
subject: Public health challenges
  topic: keywords
subject: Predictive bias
  topic: keywords
subject: Complexity
  topic: keywords
subject: Computational complexity
  topic: keywords
subject: Complex systems
  topic: keywords
subject: Algorithmic thinking and processes
  topic: keywords
subject: Mathematical Modeling
  topic: subjects
subject: AI-based mathematical modeling
  topic: subjects
subject: Differential equations
  topic: subjects
subject: Fractional calculus
  topic: subjects
subject: Fractals
  topic: subjects
subject: Applied Mathematics
  topic: subjects
subject: Computational fractal based methodologies in medicine and biology
  topic: subjects
subject: Engineering applications in medicine and biology
  topic: subjects
subject: Biomedical decision making
  topic: subjects
subject: Probabilistic clinical reasoning
  topic: subjects
subject: Stochastic processes
  topic: subjects
subject: Data analytics-based models
  topic: subjects
subject: Dynamic precision medicine
  topic: subjects
subject: Public health challenges
  topic: subjects
subject: Predictive bias
  topic: subjects
subject: Algorithmic thinking and processes
  topic: subjects
subject: Artificial Intelligence
  topic: disciplines
subject: Bioengineering
  topic: disciplines
subject: Biomedical
  topic: disciplines
subject: Biostatistics
  topic: disciplines
subject: Engineering
  topic: disciplines
subject: Machine learning
  topic: disciplines
subject: Mathematical bioengineering
  topic: disciplines
subject: Mathematical statistics
  topic: disciplines
subject: Mathematics
  topic: disciplines
subject: Medicine
  topic: disciplines
subject: Biology
  topic: disciplines
subject: Differential equations
  topic: disciplines
subject: Computational science
  topic: disciplines
subject: Computational mathematics
  topic: disciplines
subject: Computer engineering
  topic: disciplines
subject: Soft computing
  topic: disciplines
subject: Philosophy of Science
  topic: disciplines
creator: Yeliz Karaca
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 1
ending page: 11
doi: 10.69882/adba.cem.2024071

Examination and Evaluation of Obesity Risk Factors with Explainable Artificial Intelligence
type: item
description: There is an increasing need for effective methods for the detection and management of obesity, which is an important public health problem worldwide and is critical for the sustainability of health systems. This study examines the effectiveness of data preprocessing and machine learning techniques in detecting obesity. Data preprocessing steps, including the removal of unnecessary data, handling missing values, and addressing data imbalance, are necessary to enhance the accuracy of machine learning algorithms. In this study, data preprocessing steps were applied to an obesity dataset to make it suitable for machine learning. Using a dataset of 2111 patients, this study evaluates the effectiveness of machine learning techniques in detecting obesity. Following the completion of data preprocessing, obesity was identified using various machine learning algorithms, including Decision Tree Classification, Random Forest Classification, Naive Bayes Classification, KNN, and XGBoosting, and their performances were compared. According to the results, the XGBoosting algorithm exhibited the highest accuracy (0.92), precision, recall, and F1-score values. Explainable Artificial Intelligence (XAI) techniques, such as SHAP and InterpretML, were employed to understand the effects of obesity parameters and determine which parameters have a greater impact on obesity. Through visualizing and analyzing the effects of obesity parameters, these techniques facilitated the identification of significant parameters in obesity detection. The findings demonstrate that the XGBoosting algorithm outperforms other algorithms in detecting obesity. Furthermore, XAI techniques play a crucial role in comprehending obesity parameters. Specifically, a family history of obesity and factors like FCVC and CAEC appear to have more significant effects compared to others.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Obesity detection
  topic: keywords
subject: Machine learning
  topic: keywords
subject: XGBoost
  topic: keywords
subject: InterpretML
  topic: keywords
subject: explainable ai
  topic: subjects
subject: obesity detection
  topic: subjects
subject: machine learning
  topic: subjects
subject: xgboost
  topic: subjects
subject: interpretML
  topic: subjects
creator: Cem Özkurt
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 12
ending page: 17
doi: 10.69882/adba.cem.2024072