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Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. Front Neurosci 2024; 18:1237245. [PMID: 38680452 PMCID: PMC11047451 DOI: 10.3389/fnins.2024.1237245] [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: 06/09/2023] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.
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Affiliation(s)
- Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University “Hermanos Saiz Montes de Oca” of Pinar del Río, Pinar del Rio, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jorge F. Bosch-Bayard
- McGill Centre for Integrative Neurosciences MCIN, LudmerCentre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eduardo Gonzalez-Moreira
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | | | | | | | - Marlis Ontivero-Ortega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | | | | | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
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Bhimavarapu U, Chintalapudi N, Battineni G. Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier. Bioengineering (Basel) 2024; 11:266. [PMID: 38534540 DOI: 10.3390/bioengineering11030266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/28/2024] Open
Abstract
There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.
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Affiliation(s)
- Usharani Bhimavarapu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
| | - Nalini Chintalapudi
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
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Li J, Kong X, Sun L, Chen X, Ouyang G, Li X, Chen S. Identification of autism spectrum disorder based on electroencephalography: A systematic review. Comput Biol Med 2024; 170:108075. [PMID: 38301514 DOI: 10.1016/j.compbiomed.2024.108075] [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: 09/30/2023] [Revised: 12/22/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.
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Affiliation(s)
- Jing Li
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Xiaoli Kong
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Linlin Sun
- Neuroscience Research Institute, Peking University, Beijing, 100191, China; Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China, Beijing, 100191, China
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing, 100120, China; The Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100032, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shengyong Chen
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
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Zhang X, Wang S, Xu K, Zhao R, She Y. Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4779-4800. [PMID: 38549349 DOI: 10.3934/mbe.2024210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
The objective of EEG-based emotion recognition is to classify emotions by decoding signals, with potential applications in the fields of artificial intelligence and bioinformatics. Cross-subject emotion recognition is more difficult than intra-subject emotion recognition. The poor adaptability of classification model parameters is a significant factor of low accuracy in cross-subject emotion recognition. We propose a model of a dynamically optimized Random Forest based on the Sparrow Search Algorithm (SSA-RF). The decision trees number (DTN) and the leave minimum number (LMN) of the RF are dynamically optimized by the SSA. 12 features are used to construct feature combinations for selecting the optimal feature combination. DEAP and SEED datasets are employed for testing the performance of SSA-RF. The experimental results show that the accuracy of binary classification is 76.81% on DEAP, and the accuracy of triple classification is 75.96% on SEED based on SSA-RF, which are both higher than that of traditional RF. This study provides new insights for the development of cross-subject emotion recognition, and has significant theoretical value.
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Affiliation(s)
- Xiaodan Zhang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710060, China
| | - Shuyi Wang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710060, China
| | - Kemeng Xu
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710060, China
| | - Rui Zhao
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710060, China
| | - Yichong She
- School of Life Sciences, Xi Dian University, Xi'an, Shaanxi 710126, China
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Abdulahi AT, Ogundokun RO, Adenike AR, Shah MA, Ahmed YK. PulmoNet: a novel deep learning based pulmonary diseases detection model. BMC Med Imaging 2024; 24:51. [PMID: 38418987 PMCID: PMC10903074 DOI: 10.1186/s12880-024-01227-2] [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: 03/02/2023] [Accepted: 02/11/2024] [Indexed: 03/02/2024] Open
Abstract
Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.
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Affiliation(s)
- AbdulRahman Tosho Abdulahi
- Department of Computer Science, Institute of Information and Communication Technology, Kwara State Polytechnic, Ilorin, Nigeria
| | - Roseline Oluwaseun Ogundokun
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
- Department of Computer Science, Landmark University Omu Aran, Omu Aran, Nigeria
| | - Ajiboye Raimot Adenike
- Department of Statistics, Institute of Applied Sciences, Kwara State Polytechnic, Ilorin, Nigeria
| | - Mohd Asif Shah
- Department of Economics, Kebri Dehar University, Kebri Dehar, 250, Somali, Ethiopia.
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
- Chitkara Centre for Research and Development, Chitkara University, Baddi, Himachal Pradesh, 174103, India.
| | - Yusuf Kola Ahmed
- Department of Biomedical Engineering, University of Ilorin, Ilorin, Nigeria
- Department of Occupational Therapy, University of Alberta, Edmonton, Canada
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Luqman A, He M, Hassan A, Ullah M, Zhang L, Rashid Khan M, Din AU, Ullah K, Wang W, Wang G. Mood and microbes: a comprehensive review of intestinal microbiota's impact on depression. Front Psychiatry 2024; 15:1295766. [PMID: 38404464 PMCID: PMC10884216 DOI: 10.3389/fpsyt.2024.1295766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/22/2024] [Indexed: 02/27/2024] Open
Abstract
Depression is considered a multifaceted and intricate mental disorder of growing concern due to its significant impact on global health issues. The human gut microbiota, also known as the "second brain," has an important role in the CNS by regulating it through chemical, immunological, hormonal, and neurological processes. Various studies have found a significant bidirectional link between the brain and the gut, emphasizing the onset of depression therapies. The biological and molecular processes underlying depression and microbiota are required, as the bidirectional association may represent a novel study. However, profound insights into the stratification and diversity of the gut microbiota are still uncommon. This article investigates the emerging evidence of a bacterial relationship between the gut and the brain's neurological system and its potential pathogenicity and relevance. The interplay of microbiota, immune system, nervous system neurotransmitter synthesis, and neuroplasticity transitions is also widely studied. The consequences of stress, dietary fibers, probiotics, prebiotics, and antibiotics on the GB axis are being studied. Multiple studies revealed the processes underlying this axis and led to the development of effective microbiota-based drugs for both prevention and treatment. Therefore, the results support the hypothesis that gut microbiota influences depression and provide a promising area of research for an improved knowledge of the etiology of the disease and future therapies.
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Affiliation(s)
- Ameer Luqman
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, National and Local Joint Engineering Laboratory for Vascular Implant, Bioengineering College of Chongqing University, Chongqing, China
| | - Mei He
- Chongqing University Cancer Hospital, Chongqing, China
| | - Adil Hassan
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, National and Local Joint Engineering Laboratory for Vascular Implant, Bioengineering College of Chongqing University, Chongqing, China
- Chongqing Key Laboratory of Nano/Micro Composite Materials and Devices, Chongqing University of Science and Technology, Chongqing, China
- JinFeng Laboratory, Chongqing, China
| | - Mehtab Ullah
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, National and Local Joint Engineering Laboratory for Vascular Implant, Bioengineering College of Chongqing University, Chongqing, China
| | | | - Muhammad Rashid Khan
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, National and Local Joint Engineering Laboratory for Vascular Implant, Bioengineering College of Chongqing University, Chongqing, China
| | - Ahmad Ud Din
- Plants for Human Health Institute, Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Kannapolis, NC, United States
| | - Kamran Ullah
- Department of Biology, The University of Haripur, Haripur, Pakistan
| | - Wei Wang
- Chongqing University Cancer Hospital, Chongqing, China
| | - Guixue Wang
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, National and Local Joint Engineering Laboratory for Vascular Implant, Bioengineering College of Chongqing University, Chongqing, China
- JinFeng Laboratory, Chongqing, China
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Aljrees T. Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning. PLoS One 2024; 19:e0295632. [PMID: 38170713 PMCID: PMC10763959 DOI: 10.1371/journal.pone.0295632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
Cervical cancer is a leading cause of women's mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification-handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women's health and healthcare systems.
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Affiliation(s)
- Turki Aljrees
- College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia
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Liu L. Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics. Technol Health Care 2024; 32:1947-1965. [PMID: 38393861 DOI: 10.3233/thc-231517] [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] [Indexed: 02/25/2024]
Abstract
BACKGROUND Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. OBJECTIVE DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. METHOD One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. RESULTS The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. CONCLUSION The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.
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Abuzinadah N, Kumar Posa S, Alarfaj AA, Alabdulqader EA, Umer M, Kim TH, Alsubai S, Ashraf I. Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI. Cancers (Basel) 2023; 15:5793. [PMID: 38136346 PMCID: PMC10742117 DOI: 10.3390/cancers15245793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.
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Affiliation(s)
- Nihal Abuzinadah
- Faculty of Computer Science and Information Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia;
| | - Sarath Kumar Posa
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA;
| | - Aisha Ahmed Alarfaj
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ebtisam Abdullah Alabdulqader
- Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia;
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Tai-Hoon Kim
- School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50, Daehak-ro, Yeosu-si 59626, Jeollanam-do, Republic of Korea
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia;
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Javeed M, Abdelhaq M, Algarni A, Jalal A. Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things. MICROMACHINES 2023; 14:2204. [PMID: 38138373 PMCID: PMC10745656 DOI: 10.3390/mi14122204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023]
Abstract
Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.
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Affiliation(s)
- Madiha Javeed
- Department of Computer Science, Air University, Islamabad 44000, Pakistan;
| | - Maha Abdelhaq
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Asaad Algarni
- Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia;
| | - Ahmad Jalal
- Department of Computer Science, Air University, Islamabad 44000, Pakistan;
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