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Journal articles on the topic 'Secured Health Prediction'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Quazi, Warisha Ahmed, and Garg Shruti. "IoT based Smart Healthcare System in Cloud Environment." International Journal of Microsystems and IoT 1, no. 2 (2023): 73–81. https://doi.org/10.5281/zenodo.8288243.

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People are increasingly concerned about their health and need to keep it properly. The rapidly growing human population needs sophisticated systems to forecast patients' health status and appropriate treatment. The newest technological breakthroughs and innovations assist the healthcare business overcome prediction challenges. The Internet of Things (IoT) and Deep Learning technologies help transport health-related data from the local entity to the server and preserve it after evaluation. These regulations allow the medical sector to develop new health prediction technologies while saving

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Lateef Haroon P.S., Abdul, and Hareesh K N. "Feasible Implementation of Explainable AI Empowered Secured Edge Based Health Care Systems." Journal of Smart Internet of Things 2024, no. 2 (2024): 1–12. https://doi.org/10.2478/jsiot-2024-0008.

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Abstract The Infusion of Explainable Artificial Intelligence (XAI) in secured edge-based healthcare systems addresses the critical challenges of ensuring trust, transparency, and security in sensitive medical applications. Existing healthcare systems leveraging traditional AI methods often face issues such as lack of interpretability, data privacy risks, and inefficiencies in real-time decision-making. These limitations hinder user trust and the adoption of AI solutions in clinical and edge environments. To overcome these challenges, we propose an XAI-empowered secured edge-based healthcare fr

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Ghosh, Madhumita, and Ravi Gor. "HEALTH INSURANCE PREMIUM PREDICTION USING BLOCKCHAIN TECHNOLOGY AND RANDOM FOREST REGRESSION ALGORITHM." International Journal of Engineering Science Technologies 6, no. 3 (2022): 74–82. http://dx.doi.org/10.29121/ijoest.v6.i3.2022.346.

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Blockchain technology is based on a sequence of blocks, where each block carries a certain amount of information. Medical records can be cryptographically secured in the health insurance ecosystem with blockchain technology. Here, blockchain technology model is used to create a user interface for storing data block wise. Also, Insurance premium is predicted using Support Vector Regression, Lasso Regression, Ridge Regression, Multiple Linear Regression and Random Forest Regression algorithms. Out of all these algorithms, Multiple Linear Regression algorithm gives the better result.

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Munirathinam, T., Sannasi Ganapathy, and Arputharaj Kannan. "Cloud and IoT based privacy preserved e-Healthcare system using secured storage algorithm and deep learning." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 3011–23. http://dx.doi.org/10.3233/jifs-191490.

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Rapid introduction of new diseases and the severity improvement of existing dead diseases due to the bad food habits and lacking of awareness over the health conscious food items those are available in the market. The Internet of Things (IoT) gets more attention for reducing the disease severity by knowing the current status of their disease according to the dynamic inputs of human body through IoT devices today. Moreover, the combination of IoT and cloud computing technologies are playing major roles in e-health services. In this scenario, security is a major issue in the process of data stor

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Snehal, Ganesh Shinde, and L.M.R.J. Lobo Dr. "A Proposed Secured Prediction System for Human Diseases Using a Genetic Algorithm Approach to Data Mining." Journal of Data Mining and Management 3, no. 3 (2018): 25–32. https://doi.org/10.5281/zenodo.1494964.

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Many changes are happening in life styles of people in growing countries like India in recent days. Such changes in environment, diet, pollution and stress have led to the scenario that human beings are affected by microorganisms causing fatal diseases. In India, human diseases have become a major reason of deaths. A number of people have worked in this area to detect a particular disease, but it may happen that a person may be suffering from more than one disease at a time. Our attempt therefore is to detect the diseases a patient is suffering from with the use of detail symptoms given by a p

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Nalini, S., and P. Balasubramanie. "Socia media opinions aware adverse drug effect prediction and prevention system for the secured health care medical environment." Cluster Computing 22, S5 (2018): 12827–37. http://dx.doi.org/10.1007/s10586-018-1764-4.

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Hammou, Abdelilah, Boubekeur Tala-Ighil, Philippe Makany, and Hamid Gualous. "Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics." Batteries 10, no. 11 (2024): 384. http://dx.doi.org/10.3390/batteries10110384.

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The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from ex

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Karthikeyan, M., and V. Ponniyin Selvan. "A Novel Hybrid Reconfigurable Architecture for Prediction of Side Channel Attacks with Its Countermeasure Mechanism." Journal of Nanoelectronics and Optoelectronics 17, no. 7 (2022): 1056–67. http://dx.doi.org/10.1166/jno.2022.3283.

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The Internet of Things (IoT) has pushed everyone‘s normal life zone to their comfort zone by making them use embedded IoT devices for controlling and monitoring their daily gadgets. IoT devices find their applications in health care, agriculture, industrial automation, and even vehicles. Since IoT involves numerous devices’ data sharing, which causes network traffic and makes them vulnerable to security breaches, especially Side-Channel Attacks (SCA), it creates demand for an intelligent framework. As of now, many secured cryptoengines are integrated into embedded chips, but still, SCAs play a

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Qureshi, Salim Raza. "An Enhanced Framework To Secure Big Data Based on Hybrid Machine Learning Technique:ANN-PSO." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 76–84. http://dx.doi.org/10.35940/ijrte.f5385.039621.

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With the advancement of smart devices and cloud computing, more and more public health data can be collected from various sources and analyzed in unprecedented ways. The enormous social and academic impact of this development has led to a global buzz for bigdata. Moreover, due to the massive data source, the security of big data in the cloud is becoming an important issue. In these days, various issues have arisen in the field of big data security, such as Infrastructure security, data confidentiality, data management and data integrity. In this paper, we propose a novel technique based on Art

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Assoc., Prof. Salim Raza Qureshi. "An Enhanced Framework To Secure Big Data Based on Hybrid Machine Learning Technique:ANN-PSO." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 6 (2021): 76–84. https://doi.org/10.35940/ijrte.F5385.039621.

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<strong>Abstract:</strong> With the advancement of smart devices and cloud computing, more and more public health data can be collected from various sources and analyzed in unprecedented ways. The enormous social and academic impact of this development has led to a global buzz for bigdata. Moreover, due to the massive data source, the security of big data in the cloud is becoming an important issue. In these days, various issues have arisen in the field of big data security, such as Infrastructure security, data confidentiality, data management and data integrity. In this paper, we propose a n

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Jillahi, Kamal Bakari, and Aamo Iorliam. "A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends." Journal of Computing Theories and Applications 1, no. 4 (2024): 421–45. http://dx.doi.org/10.62411/jcta.10350.

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Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting

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Lavanya K. "A Review on Machine Learning and Deep Learning Techniques for Medical Internet of Things (m-IoT)." Communications on Applied Nonlinear Analysis 32, no. 2s (2024): 271–83. https://doi.org/10.52783/cana.v32.2384.

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The mobile health care over Internet of Things (IoT) offers flexibility and fast clinical diagnosis irrespective of distance and viewing displays. The delivery, management, and oversight of medical services have undergone a radical transformation as a result of the integration of Machine Learning (ML) and Deep Learning (DL) techniques in the mobile healthcare domain. To achieve better Quality of Service (QoS), the present networks needs more lime light of research in terms of streaming the medical videos without sacrificing the medical quality of experience (mQoE).Researchers have addressed va

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Min, Kyung Suk. "A Study on the Application of Smart Technology to Improve the Safety of Smart Cities." Forum of Public Safety and Culture 24 (September 30, 2023): 167–85. http://dx.doi.org/10.52902/kjsc.2023.24.167.

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In order to propose ways to apply smart technology to improve the safety of smart cities, this study aims to propose smart technologies that are highly related to safety management in the areas of transportation, medical care, environment, nature, and crime prevention in smart cities, such as AR, BIM, big data, and cloud. , IoT, VR, robot automation, drones, and 3D printing were analyzed. For the study, the applicability of smart technology in each field was investigated by safety management experts using a matrix evaluation method, and the results can be summarized as follows. In the transpor

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Brindha Devi, V., Lokeswari U, Saraswathi B, and Vindhya T. "An effective cloud based personal emergency response system by providing privacy protection for the medical data." International Journal of Engineering & Technology 7, no. 3.3 (2018): 261. http://dx.doi.org/10.14419/ijet.v7i2.33.14165.

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Timely access to the emergency medical services is challenging tasks due to the increasing percentage of population. Especially prehospital emergency situations are neglected for quite a long time. The agglomeration of medical gadgets and other system applications that bridges the gap to healthcare IT systems through Internet or computer networks is placed under the domain (IoMT) Internet Of Medical Things . In this project an efficient medical data monitoring and an emergency response system has been developed. IoT in healthcare is made to bridge the gap by providing the connectivity through

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Ali Hadi Abdulwahid. "IoT-Based Hybrid Fuzzy LSTM-RNN for Secure Disease Prediction in Healthcare EHRs." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 339–56. https://doi.org/10.52783/jisem.v10i36s.6438.

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The integration of Fuzzy Logic and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) is employed to handle healthcare data, leading to a significant improvement in the prediction of unknown disease outcomes and notably enhancing reliability and accuracy. In this research, we propose an integrated IoT-based healthcare data management system with Fuzzy Long Short-Term Memory Recurrent Neural Network (IF-LSTM-RNN) for disease prediction and diagnosis. Our approach includes gathering data via IoT devices, preprocessing through min-max normalization, and utilizing IF-LSTM-RNN for predicti

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Prasad, V. Maruthi, and B. Bharathi. "An Internet of Medical Things and Deep Learning-Based Intelligent Security Framework for Heart Health." Indian Journal Of Science And Technology 18, no. 26 (2025): 2100–2110. https://doi.org/10.17485/ijst/v18i26.3485.

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Objective: This study aims to address the growing network security risks posed by the increasing integration of IoMT infrastructure into medical facilities. The main goal is to develop and propose the Duo-Secure IoMT architecture that effectively counters complex cybersecurity threats such as botnet-driven distributed denial of service (DDoS) attacks and zero-day attacks while maintaining the integrity of clinical data. Methods: The Duo-Secure IoMT framework uses multimodal sensory cues to differentiate between typical IoMT data behaviour and patterns of malicious attacks. To analyze sensory d

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Ghajari, Yasser Ebrahimian, Mehrdad Kaveh, and Diego Martín. "Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth." Mathematics 11, no. 19 (2023): 4145. http://dx.doi.org/10.3390/math11194145.

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Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, su

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Upadhyay, Shrikant, Mohit Kumar, Ashwani Kumar, et al. "Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy." Computational and Mathematical Methods in Medicine 2022 (July 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/8717263.

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Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People’s mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human’s various internal reflections can be expected and extracted by focusing on the vo

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Jaganathan, Gowthami, and Shanthi Natesan. "Blockchain and explainable-AI integrated system for Polycystic Ovary Syndrome (PCOS) detection." PeerJ Computer Science 11 (February 28, 2025): e2702. https://doi.org/10.7717/peerj-cs.2702.

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In the modern era of digitalization, integration with blockchain and machine learning (ML) technologies is most important for improving applications in healthcare management and secure prediction analysis of health data. This research aims to develop a novel methodology for securely storing patient medical data and analyzing it for PCOS prediction. The main goals are to leverage Hyperledger Fabric for immutable, private data and to integrate Explainable Artificial Intelligence (XAI) techniques to enhance transparency in decision-making. The innovation of this study is the unique integration of

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Yousef, Consuela C., Teresa M. Salgado, Ali Farooq, et al. "Predicting Health Care Providers' Acceptance of a Personal Health Record Secure Messaging Feature." Applied Clinical Informatics 13, no. 01 (2022): 148–60. http://dx.doi.org/10.1055/s-0041-1742217.

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Abstract Background Personal health records (PHRs) can facilitate patient-centered communication through the secure messaging feature. As health care organizations in the Kingdom of Saudi Arabia implement PHRs and begin to implement the secure messaging feature, studies are needed to evaluate health care providers' acceptance. Objective The aim of this study was to identify predictors of health care providers' behavioral intention to support the addition of a secure messaging feature in PHRs using an adapted model of the Unified Theory of Acceptance and Use of Technology as the theoretical fra

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R., R., and K. Santhosh Kumar. "A Novel Blockchain-Enabled Fuzzy CLSTM Model for Secure and Scalable Heart Disease Prediction in Healthcare." Fusion: Practice and Applications 18, no. 2 (2025): 262–75. https://doi.org/10.54216/fpa.180219.

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The emerging field of healthcare has taken severe measures to safeguard sensitive patient health-related information especially the information taken from the predictive model. In this study, a novel blockchain-based solution is proposed in correlation with the Fuzzy-enhanced CLSTM model (FCLSTM) for storing and transmitting the data securely for heart disease prediction systems by ensuring data integrity, confidentiality, and access control. The proposed model uses a blockchain-based network which is implemented to prevent the tampering or unauthorized access to patients’ health-related data.

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Buchanan, Alec, and Morven Leese. "Quantifying the contributions of three types of information to the prediction of criminal conviction using the receiver operating characteristic." British Journal of Psychiatry 188, no. 5 (2006): 472–78. http://dx.doi.org/10.1192/bjp.bp.105.011122.

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BackgroundQuantifying the contributions that different types of information make to the accurate prediction of offending offers the prospects of improved practice and better use of resources.AimsTo quantify the contributions made by three types of information – demographic data alone, demographic and criminal record and demographic, criminal record and legal class of disorder – to the prediction of criminal conviction in patients.MethodAll 425 patients discharged from the three special (high secure) hospitals in England and Wales over 2 years were followed for 10.5 years. The contribution of e

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Khodadadi, Ehsaneh, and S. K. Towfek. "Internet of Things Enabled Disease Outbreak Detection: A Predictive Modeling System." Journal of Intelligent Systems and Internet of Things 10, no. 1 (2023): 84–91. http://dx.doi.org/10.54216/jisiot.100107.

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Advancements in data analytics and the proliferation of the Internet of Things (IoT) have opened new frontiers in disease surveillance and early outbreak detection. In this paper, we present a comprehensive framework that integrates IoT-driven predictive data analytics with a secure blockchain network to revolutionize the early warning of disease outbreaks. Our system model comprises edge devices equipped with sensors for data collection and processing, coupled with a blockchain network ensuring data integrity and transparency. Within this framework, we focus on the pivotal role of a Support V

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Zhang, Zhaosheng, Shiji Dong, Da Li, Peng Liu, and Zhenpo Wang. "Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest." Processes 12, no. 1 (2024): 136. http://dx.doi.org/10.3390/pr12010136.

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Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers

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Ezeana, Chika, Xiaohui Yu, Zhihao Wan, et al. "Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients." Cancer Research 84, no. 9_Supplement (2024): PO2–28–06—PO2–28–06. http://dx.doi.org/10.1158/1538-7445.sabcs23-po2-28-06.

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Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessar

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Siddique, Sumaiya. "Health Information Exchange using BlockChain and Cardiac Disease Prediction using Naïve Bayes Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3215–26. http://dx.doi.org/10.22214/ijraset.2022.45780.

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Abstract: The interchange of electronic health data across healthcare facilities is made possible via the health information exchange program. There is a potential for data manipulation in this. This article primarily focuses on using "Blockchain," i.e. one of the greatest technologies, to secure medical health data. Blockchain has demonstrated its outstanding qualities in the field of cryptocurrencies like bitcoin and Ethereum. This study employs the Secure Hash Algorithm (SHA), Simple Mail Transfer Protocol (SMTP), and AES Rijndael Algorithm (SMTP). Additionally, using the Naïve Bayes method

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Murikipudi, Abhishek. "Public Health Crisis Management using Java And AI." International Journal of Enhanced Research in Management & Computer Applications 14, no. 02 (2025): 25–32. https://doi.org/10.55948/ijermca.2025.0205.

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The research focused on the use of AI and secure data processing in managing the public health crisis. It proposed a Java-based system that provides predictive analytics and real-time monitoring that can support crisis responses. SQL and NoSQL databases have been implemented for the management of structured and unstructured public health data, respectively ensuring efficiency in storage and retrieval. AI-driven prediction mechanisms help analyze the crisis trends and resource allocations to make better decisions. The research has evolved involving the system’s scalable architecture and secure

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Zhao, Huiya, Dehao Sui, Yasha Wang, Liantao Ma, and Ling Wang. "Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction." Sensors 25, no. 8 (2025): 2374. https://doi.org/10.3390/s25082374.

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Secure and privacy-preserving health status representation learning has become a critical challenge in clinical prediction systems. While deep learning models require substantial high-quality data for training, electronic health records are often restricted by strict privacy regulations and institutional policies, particularly during emerging health crises. Traditional approaches to data integration across medical institutions face significant privacy and security challenges, as healthcare providers cannot directly share patient data. This work presents MultiProg, a secure federated learning f

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Wolf, A., T. R. Fanshawe, A. Sariaslan, R. Cornish, H. Larsson, and S. Fazel. "Prediction of violent crime on discharge from secure psychiatric hospitals: A clinical prediction rule (FoVOx)." European Psychiatry 47 (January 2018): 88–93. http://dx.doi.org/10.1016/j.eurpsy.2017.07.011.

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AbstractBackgroundCurrent approaches to assess violence risk in secure hospitals are resource intensive, limited by accuracy and authorship bias and may have reached a performance ceiling. This study seeks to develop scalable predictive models for violent offending following discharge from secure psychiatric hospitals.MethodsWe identified all patients discharged from secure hospitals in Sweden between January 1, 1992 and December 31, 2013. Using multiple Cox regression, pre-specified criminal, sociodemographic, and clinical risk factors were included in a model that was tested for discriminati

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Bala, Bala, Deepak Dudeja, Sonia Duggal, Sachin Sharma, Anupriya Jain, and Piyush Kumar Pareek. "Cyber Security Based Application-Specific Integrated Circuit for Epileptic Seizure Prediction Using Convolutional Neural Network." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 234–50. http://dx.doi.org/10.54216/jisiot.130117.

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In the event of an epileptic attack, the Field-Programmable Gate Array (FPGA)-accelerated Convolutional Neural Network (CNN) model is paired with Electroencephalogram (EEG) acquisition equipment to produce a reliable production system that can be used in clinical medical diagnosis. Additionally, this study includes cybersecurity to protect both the epileptic patient’s data and the prediction system. Epilepsy is a frequent neurological disorder that manifests as recurrent seizures, a sign that indicates rapid intervention is necessary to minimize adverse events and improve patient health. The s

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Bhullar, Jaideep Singh. "Developing a Smart Health Monitoring and Anomaly Detection by leveraging Internet of Things (IoT) and Artificial Intelligence (AI)." International Journal of Research in Medical Sciences and Technology 15, no. 1 (2023): 96–100. https://doi.org/10.37648/ijrmst.v15i01.014.

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With the fast-paced growth of the Internet of Things (IoT) and Artificial Intelligence (AI), the healthcare industry is shifting to a paradigm of smart health monitoring systems with IoT-enabled medical devices and AI-driven analytics. These health monitoring systems, by combining the power of IoT and AI together in the healthcare value chain, help to improve real-time patient monitoring for early anomalies and allow personalized and timely interventions. IoT-enabled medical devices gather round-the-clock patient data: heart rate, oxygen saturation, glucose levels, ECG patterns, among others.

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COID, J., N. KAHTAN, A. COOK, S. GAULT, and B. JARMAN. "Predicting admission rates to secure forensic psychiatry services." Psychological Medicine 31, no. 3 (2001): 531–39. http://dx.doi.org/10.1017/s003329170100366x.

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Background. The planning and development of secure forensic psychiatry services for mentally disordered offenders in England and Wales has proceeded independently within different regional areas. However, certain mental disorders, offenders, and offending behaviour are all more prevalent in geographical areas characterized by socio-economic deprivation and social disorganization. Failure to consider these factors has led to inadequate service provision in some areas and inequity in funding. A new model is required to predict admissions to these services as an aid to resource allocation.Method.

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Harshini and Srithar. "An Analytical Predictive Model and Secure Wed Based Personalized Diabetes Monitoring System using Stacking Ensemble Classification." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 967–75. http://dx.doi.org/10.47392/irjaeh.2024.0135.

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As the number of people diagnosed with diabetes continues to rise, this study takes a groundbreaking approach by developing a secure web-based personalized diabetes monitoring system that incorporates analytical prediction models. This groundbreaking technology was developed in response to the critical need for sophisticated monitoring solutions that address the unique demands of each patient. The suggested method aims to transform diabetes treatment by using predictive modeling to anticipate diabetic trends and possible consequences. The study demonstrates a strong commitment to security by c

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Kaewkerd, Onuma, Pranom Othaganont, and Christine L. Williams. "A Mixed-Method Approach on Secure Attachment and its Effects on Caregivers of Older Adults Living at Home." Open Public Health Journal 14, no. 1 (2021): 71–78. http://dx.doi.org/10.2174/1874944502114010071.

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Background: A secure attachment style of informal caregivers is important for the care of older adults at home. Informal caregivers who have secure attachment style to care for older adults, can effectively provide care for older adults. Objective: A sequential explanatory mixed-method design was introduced to study the factors predicting secure attachment and explain informal caregivers’ perceptions. Materials and Methods: 140 informal caregivers were selected from sub-district health-promoting hospitals from provinces in the northeastern Thailand by using the multi-stage random sampling meth

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Jayendra S. Jadhav. "A Decentralized Blockchain-Node-Red-Cloud Architecture for Secure EHR Management and Novel Disease Prediction." Journal of Information Systems Engineering and Management 10, no. 50s (2025): 718–39. https://doi.org/10.52783/jisem.v10i50s.10353.

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In today’s healthcare landscape, where data is the backbone of effective care and seamless supply chains are essential, system inefficiencies and data vulnerabilities can seriously impact patient safety. This paper introduces a novel, integrated framework that combines Blockchain technology, Node-RED, and cloud computing to transform how healthcare data is managed and how emerging diseases are detected. A private Blockchain acts as the backbone for securely storing electronic health records (EHRs) and tracking supply chain activities, ensuring data integrity and traceability. To streamline rea

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Jayendra S. Jadhav. "A Decentralized Blockchain-Node-Red-Cloud Architecture for Secure EHR Management and Novel Disease Prediction." Journal of Information Systems Engineering and Management 9, no. 4s (2024): 364–83. https://doi.org/10.52783/jisem.v9i4s.11203.

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In today’s healthcare landscape, where data is the backbone of effective care and seamless supply chains are essential, system inefficiencies and data vulnerabilities can seriously impact patient safety. This paper introduces a novel, integrated framework that combines Blockchain technology, Node-RED, and cloud computing to transform how healthcare data is managed and how emerging diseases are detected. A private Blockchain acts as the backbone for securely storing electronic health records (EHRs) and tracking supply chain activities, ensuring data integrity and traceability. To streamline rea

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Yin, Fagen, Pingping Xiao, and Zefeng Li. "ASC Performance Prediction for Medical IoT Communication Networks." Journal of Healthcare Engineering 2021 (May 27, 2021): 1–7. http://dx.doi.org/10.1155/2021/6265520.

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Wearable devices are gradually entering the medical health field. Medical Internet of Things (IoT) has been widely used in all walks of medical health. With the complexity of medical health application scenarios, the medical IoT communication networks face complex environments. The secure communication issue is very important for medical IoT communication networks. This paper investigates the secrecy performance of medical IoT communication networks. To improve the secrecy performance, we adopt a cooperative communication strategy. We also use the average secrecy capacity (ASC) as a metric, an

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Kumar, Mohit, Priya Mukherjee, Sahil Verma, et al. "BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems." Sensors 22, no. 23 (2022): 9448. http://dx.doi.org/10.3390/s22239448.

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The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction

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Beer, Dominic, Mary Jane Spiller, Max Pickard, et al. "Low secure units: Factors predicting delayed discharge." Journal of Forensic Psychiatry & Psychology 16, no. 4 (2005): 621–37. http://dx.doi.org/10.1080/14789940500159475.

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Mortimer, Ann. "Reducing violence on a secure ward." Psychiatric Bulletin 19, no. 10 (1995): 605–8. http://dx.doi.org/10.1192/pb.19.10.605.

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Prediction of in-patient psychiatric violence is difficult: longitudinal appraisal during environmental change may identify Influential factors. Incidents on a secure ward fell substantially in number and severity over 31 months during which staff were trained in control and restraint techniques (CAR) and a monthly audit of incidents was carried out. A few patients caused many incidents. Women were disproportionately violent: both sexes preferred a victim of the same gender. Most incidents occurred in clusters by the same patient. Perceived antecedents were patients' psychosis, inadequate CAR

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Hameed, Kashif, Imran Sarwar Bajwa, Nadeem Sarwar, Waheed Anwar, Zaigham Mushtaq, and Tayyaba Rashid. "Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT." Journal of Healthcare Engineering 2021 (March 22, 2021): 1–18. http://dx.doi.org/10.1155/2021/8814364.

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The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of

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Ijaz, Muhammad, Ganjar Alfian, Muhammad Syafrudin, and Jongtae Rhee. "Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest." Applied Sciences 8, no. 8 (2018): 1325. http://dx.doi.org/10.3390/app8081325.

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As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the dise

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Kiruthiga, V., and Dr K. Lakshmi Priya. "A Privacy-Preserving Framework for Mental Health Prediction Using Federated Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1474–80. https://doi.org/10.22214/ijraset.2025.73218.

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The early detection of mental health disorders, particularly depression and anxiety, is essential for timely intervention and improved patient outcomes. Recent advances in Artificial Intelligence (AI) have enabled the development of systems capable of identifying psychological distress through multimodal data, including text, audio, and facial expressions. However, conventional AI models typically rely on centralized data collection, which poses significant risks to user privacy and data security, especially in healthcare applications involving sensitive personal information. This paper propos

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de Batlle, Jordi, Ivan D. Benítez, Anna Moncusí-Moix, et al. "GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases." Journal of Medical Internet Research 25 (June 28, 2023): e42187. http://dx.doi.org/10.2196/42187.

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Background The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective We aim to describe the rationale for

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Kulkarni, Madhavi. "Health Chain: Kidney Liver Disease Diagnosis with Secure Organ Donation Using Blockchain." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49090.

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Abstract- —This paper presents an integrated framework that leverages blockchain techniques and machine learning for the diagnosis of Kidney Disease and liver diseases. Combining blockchain-based decentralized organ donation systems with advanced machine learning models ensures accurate predictions, secure health record management, and efficient organ donation. We employ statistical feature extraction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to improve disease classification, while blockchain ensures the security and transparency of the organ

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Francis, Kara-Rose. "Attachment and mental health utilisation: A police perspective." BPS Branch Awards 2, no. 1 (2024): 43–45. http://dx.doi.org/10.53841/bpsba.2024.2.1.43.

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At present, many Police officers avoid support for mental illness due to perceived occupational stigma (POS), depleting their wellbeing and job satisfaction. The present study examined the predictive qualities of attachment type, attempting to identify those predisposed to avoiding support based upon help-seeking attitudes, intentions and POS. According to 153 survey responses, POS significantly mediates the relationship between help-seeking attitudes and intentions in both secure and insecure-avoidant UK Police officers. While attachment based predictions were not supported, findings do empha

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Stevens, Heather B., and Stanley L. Brodsky. "Perceived Consequences to the Predictor: A Variable in the Release of Psychiatric Patients." Psychological Reports 76, no. 3_suppl (1995): 1371–78. http://dx.doi.org/10.2466/pr0.1995.76.3c.1371.

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The present study examined factors hypothesized to influence mental health professionals' perceptions of dangerousness, predictions of violence, and decisions on patients' release. 120 mental health professionals employed in state mental hospitals were each given one of 12 patient profiles. The independent variables, manipulated within vignettes, were (a) violence history, (b) paranoid schizophrenia versus nonparanoid schizophrenia, and (c) perceived consequences in terms of liability and publicity. Type of schizophrenia did not affect ratings, but violence history of the predictee and perceiv

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Su, Yifei, Chengwei Huang, Wenwei Zhu, Xin Lyu, and Fang Ji. "Multi-party Diabetes Mellitus risk prediction based on secure federated learning." Biomedical Signal Processing and Control 85 (August 2023): 104881. http://dx.doi.org/10.1016/j.bspc.2023.104881.

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Reimann, Bradley J., and David Nussbaum. "Predicting Seclusion in a Medium Secure Forensic Inpatient Setting." International Journal of Forensic Mental Health 10, no. 2 (2011): 150–58. http://dx.doi.org/10.1080/14999013.2011.578299.

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Suneetha, Dr A. "Deep Learning and Blockchain for Accurate Skin Cancer and Disease Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42750.

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Skin cancer is a significant global health concern, primarily caused by excessive exposure to ultraviolet (UV) radiation. If not detected and treated early, skin cancer can spread to vital organs such as the lungs, brain, and liver, complicating treatment and reducing survival rates. Early detection is crucial for maximizing recovery chances and improving patient outcomes. This research proposes a deep learning-based application to enhance the accuracy and efficiency of skin cancer and disease prediction. This study combines Artificial Intelligence (AI) and blockchain technology to create a se

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