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smart healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)

Computers, Materials & Continua 2021年第1期66卷 589-602页

作者： Sadaf Qazi Muhammad Usman Azhar Mahmood Aaqif Afzaal Abbasi Muhammad Attique Yunyoung Nam Predicitive Analytics Laboratory Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Software Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and *** Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free *** study proposes a defaulter prediction model for accurate identification of *** proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization *** machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)*** Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being *** this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive ***,predictive analytics can reinforce immunization programs by expediting targeted action to reduce ***,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.

关键词： smart healthcare routine immunization predictive analytics defaulters vaccination machine learning targeted messaging

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Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled smart healthcare System

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Computers, Materials & Continua 2023年第4期75卷 1353-1369页

作者： Sarah B.Basahel Saleh Bajaba Mohammad Yamin Sachi Nandan Mohanty E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart *** services could be enhanced by incorporating key techniques like AI and *** convergence of AI and IoT provides distinct opportunities in the medical *** is regarded as a primary cause of death or post-traumatic complication for the ageing ***,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary ***,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home *** article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled smart healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare ***,the presented TWODL-FDSHS technique exploits IoT devices for the data collection ***,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature *** last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS *** experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

关键词： Internet of things smart healthcare deep learning team work optimizer capsnet fall detection

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Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in smart healthcare

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Current Medical Science 2021年第6期41卷 1123-1133页

作者： Yi XIE Lin LU Fei GAO Shuang-jiang HE Hui-juan ZHAO Ying FANG Jia-ming YANG Ying AN Zhe-wei YE Zhe DONG Department of Orthopedic Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Laboratory of Intelligent Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China Wuhan Fourth Hospital Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430032China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China

Chronic diseases are a growing concern worldwide,with nearly 25% of adults suffering from one or more chronic health conditions,thus placing a heavy burden on individuals,families,and healthcare *** the advent of the“smart healthcare”era,a series of cutting-edge technologies has brought new experiences to the management of chronic *** them,smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic ***,how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management,in terms of quality of life,patient outcomes,and privacy protection,is an urgent issue that needs to be *** intelligence(AI)can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of *** addition,blockchain can improve healthcare services by authorizing decentralized data sharing,protecting the privacy of users,providing data empowerment,and ensuring the reliability of data *** AI,blockchain,and wearable technology could optimize the existing chronic disease management models,with a shift from a hospital-centered model to a patient-centered *** this paper,we conceptually demonstrate a patient-centric technical framework based on AI,blockchain,and wearable technology and further explore the application of these integrated technologies in chronic disease ***,the shortcomings of this new paradigm and future research directions are also discussed.

关键词： artificial intelligence blockchain wearable technology/devices chronic diseases smart healthcare health monitoring personalization healthcare management patient-centric

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Telemedicine and smart healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

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International Journal of Communications, Network and System Sciences 2023年第3期16卷 31-51页

作者： Taofik Ahmed Suleiman Abdulkareem Adinoyi Department of Medical Imaging and Computing University of Girona Girona Spain Graduate School of Science and Technology Medical Imaging and Applications (MAIA) University of Burgundy Le Creusot France Department of Electrical and Information Engineering MAIA University of Cassino and Southern Lazio Cassino Italy Research and Innovation Department academy from stc Saudi Telecommunication Company (stc) Riyadh Saudi Arabia

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

关键词： Telemedicine smart healthcare 5G Artificial Intelligence Machine Learning Internet-of-Medical-Things

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Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on smart healthcare Applications

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Computer Systems Science & Engineering 2023年第9期46卷 3749-3765页

作者： Iyad Katib Mahmoud Ragab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The concept of smart healthcare has seen a gradual increase with the expansion of information *** healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more *** work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare *** presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare *** the presented HBO-DQNN model,three stages of operations were *** normalization is applied to pre-process the input data at the initial ***,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare *** last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN *** extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

关键词： Heap-based optimization smart healthcare decision making intelligent models artificial intelligence

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healthcare data analysis system for regional medical union in smart city

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Journal of Management Analytics 2018年第4期5卷 334-349页

作者： Boyi Xu Ling Li Daiping Hu Bin Wu Congcong Ye Hongming Cai Antai College of Economics&Management Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China College of Business&Public Administration Old Dominion UniversityNorfolkVAUSA Renji Hospital ShanghaiPeople’s Republic of China School of Software Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

Regional medical unions are practical approaches to deal with the cases that patients crowd in Grade 3 Class A general hospitals in metropolitan cities such as Shanghai,in ***,electronic medical data analysis exists challenges when patients are referred among different hospitals in the regional medical unions during treatment *** smart cities,demands for medical services provided by smart devises,complicate the environment of medical data *** order to tackle the above problems,in this paper,a healthcare data analysis system for regional medical union is designed to support doctors from different hospitals to assess health conditions of patients in an overall data *** patterns are mined from physiological index *** are generated from social networks data to find the hot topics concerned by people living in common *** are given to illustrate the feasibility of the system in supporting healthcare data analysis.

关键词： smart healthcare electronic healthcare records behaviour pattern mining regional medical union

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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Computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

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Computers, Materials & Continua 2022年第8期72卷 3945-3959页

作者： Romany F.Mansour Maha M.Althobaiti Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical ***,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare *** such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data ***,breast cancer(BC)has been found to be a major cause of mortality among ladies *** detection and classification of BC using digital mammograms can decrease the mortality *** paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT *** goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify *** proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital *** addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the ***,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented *** enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM *** DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

关键词： Cognitive computing breast cancer digital mammograms image processing internet of medical things smart healthcare

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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