Relevant bibliographies by topics / Secured Health Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Secured Health Prediction'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Secured Health Prediction"

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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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Dissertations / Theses on the topic "Secured Health Prediction"

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O'Shea, Laura E. "Structured professional judgement approach to risk assessment : generalisability across patient groups for the prediction of adverse outcomes in secure mental health care." Thesis, Abertay University, 2016. https://rke.abertay.ac.uk/en/studentTheses/323a84a6-d0f2-42ab-9225-bc83eee53b83.

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This thesis comprises a rigorous and coherent body of work related to the use of the HCR-20 and the START to inform risk assessment and management of secure mental health inpatients. The thesis contributes significant theoretical and applied knowledge by: 1) investigating the extent to which these tools can be generalised beyond restricted validation samples to the full range of individuals in contact with secure services, 2) determining whether they can aid assessment and management of adverse outcomes beyond aggression, and 3) offering practical, empirically-derived advice for clinicians reg
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Book chapters on the topic "Secured Health Prediction"

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Gupta, Sunil, Hitesh Kumar Sharma, and Monit Kapoor. "Machine Learning for predictive analytics in Smart health and Virtual care." In Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18896-1_14.

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Ravi Shanker Reddy, T., and B. M. Beena. "AI Integrated Blockchain Technology for Secure Health Care—Consent-Based Secured Federated Transfer Learning for Predicting COVID-19 on Wearable Devices." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2821-5_30.

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Bakhsh, Shahid Allah, Fawad Ahmed, Muhammad Almas Khan, et al. "A secure dockerized architecture using IIoT sensors predicting machine health and maintenance." In Cybersecurity, Cybercrimes, and Smart Emerging Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003614197-19.

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Padhya, Bhargav P., Jyotindra N. Dharwa, Himanshu N. Patel, and Kashyap C. Patel. "A Secure Health Monitoring Model for Prediction of Heart Disease Detection Using Machine Learning." In ICT: Innovation and Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9486-1_25.

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Gudivaka, Rajya Lakshmi, Sri Harsha Grandhi, Basava Ramanjaneyulu Gudivaka, Raj Kumar Gudivaka, Dinesh Kumar Reddy Basani, and G. Arulkumaran. "IoT-Powered IPMS and AIPRA Revolutionize Healthcare With AI-Driven Pandemic Detection, Resource Optimization, Remote Monitoring, and Global Health." In Convergence of Blockchain, Internet of Everything, and Federated Learning for Security. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1424-2.ch010.

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Background Information: Pandemics severely challenge global health care systems. IPMS and AIPRA through IoT, AI, and blockchain are capable of real-time detection, resource optimization, and secured data exchange. Objectives: This paper aims at enhancing preparedness in pandemics and optimization in the usage of resources, allowing proactive surveillance, and application of IoT and AI-driven sustainable solutions to address concerns of scalability, interoperability, and privacy issues. Methods: Resource management, data-driven pandemic prediction, remote monitoring, and global health operation
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Undavia, Jaimin N., Atul Patel, and Sheenal Patel. "Security Issues and Challenges Related to Big Data." In Big Data Management and the Internet of Things for Improved Health Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5222-2.ch006.

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Availability of huge amount of data has opened up a new area and challenge to analyze these data. Analysis of these data become essential for each organization and these analyses may yield some useful information for their future prospectus. To store, manage and analyze such huge amount of data traditional database systems are not adequate and not capable also, so new data term is introduced – “Big Data”. This term refers to huge amount of data which are used for analytical purpose and future prediction or forecasting. Big Data may consist of combination of structured, semi structured or unstr
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Jabeen, Muqadsa, Muhammad Ibrar, Majid Hussain, and Muhammad Arshad Shehzad Hassan. "Blockchain-Based Explainable AI for Secure and Privacy-Preserving Automated Machine Learning in IoT-Edge for Smart Medical Healthcare." In AI and Blockchain Applications for Privacy and Security in Smart Medical Systems. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0593-6.ch003.

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Coronary Heart Disease (CHD) continues to affect close to 145 million males and 110 million females across the globe, taking close to nine million lives each year. A new evolving framework incorporating IoT-edge computing, Explainable AI, and blockchain technology is in the process of development in order to create a secure, privacy-preserving, and interpretable automated machine learning environment to predict chronic diseases. The IoT-based system utilizes medical sensors and assistive devices for real-time monitoring of the patient. The patient information is transmitted securely using bloc
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Chauhan, Ritu, and Harleen Kaur. "Predictive Analytics and Data Mining." In Advances in Secure Computing, Internet Services, and Applications. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4940-8.ch004.

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High dimensional databases are proving to be a major concern among the researches to extract relevant information for futuristic decision making. Real world data is high dimensional in nature and comprises of irrelevant features, missing values, and redundancy, which requires serious concerns. Utilizing all such features can mislead the results for emergent prediction. Therefore, such databases are critical in nature to determine optimal solutions. To deal with such issues, the authors have developed and implemented a Cluster Analysis Study Behavior of School Children from Large Databases (CAB
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Babu, C. V. Suresh, Logapadmini B. A., Kavin Regash, and R. Biruntha. "Cybersecurity and Privacy Concerns in Digital Health." In Navigating Innovations and Challenges in Travel Medicine and Digital Health. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8774-0.ch020.

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The chapter investigates the critical intersection of digital health technologies and travel medicine, emphasizing the cybersecurity and privacy challenges inherent in this integration. Aimed at researchers, policymakers, academicians, and technology developers, the study evaluates vulnerabilities associated with digital health tools such as telemedicine, wearable devices, and AI-driven analytics. Employing qualitative analysis of case studies and quantitative user surveys, the chapter identifies gaps in regulatory frameworks and technological interoperability. Findings highlight the transform
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Reisdorf, Bianca C., and Julia R. DeCook. "The Digital Technologies of Rehabilitation and Reentry." In The Pre-Crime Society. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529205251.003.0017.

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The period immediately following prison release is challenging, as returning citizens need to obtain housing, find employment, secure health care, and reconnect with families. In addition to having a criminal record, returning citizens encounter a technologically savvy and hyper-secured society that requires use of touchscreens, mobile phones, computers, and the Internet, and relies heavily on dataveillance and big data analysis for predictive policing and crime-prevention. Most returning citizens do not have the digital access and skills needed for employment, access to online human and socia
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Conference papers on the topic "Secured Health Prediction"

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R, Velvizhi, Bhooma Charitha, Bala Yashwitha Kasarapu, and Manju C. Nair. "Heart Disease Prediction using Secure Decentralized Data Sharing." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013421.

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Bulasara, Kavya Sree Sai, Sai Sailu Batta, Mahesh Miriyala, and Veerapu Goutham. "Hybrid Federated Learning for Secure and Accurate Heart Disease Prediction." In 2025 International Conference on Power Electronics Converters for Transportation and Energy Applications (PECTEA). IEEE, 2025. https://doi.org/10.1109/pectea61788.2025.11076242.

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Bhorge, Siddharth, Vijay Mane, Uma S. Salunkhe, Sankalp Sunil Savane, Shubham Nilesh Puniwala, and Vedant Nitin Rane. "Predictive Maintenance System with Secure Data Transmission using Machine Learning and Signal Processing Techniques for Vibration Analysis." In 2025 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE, 2025. https://doi.org/10.1109/amathe65477.2025.11081320.

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"Extraction of Parameters for 90-degree Turn Prediction Using the IMU-based Motion Capture System." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-29.

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Abstract. Against the increasing number of single households, we have been proposing the “Biofied Building” that provides a safe, secure, and comfortable living space for a resident using a small home robot. The robot can be used for real-time sensing of the resident’s position and behavior. On the other hand, for further use of the robot, such as choosing a path that does not disturb the resident, a phase to predict the resident’s behavior is necessary. Walking, which is one of the most basic activities of daily living, is often targeted in studies of motion prediction. However, most of them
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Bhushan, Megha, Aariyan Sahu, Gracee Ranjan, Subham Sharma, Khushi Sharma, and Arun Negi. "Prediction Of Health Status Using Machine Learning Techniques." In 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, 2023. http://dx.doi.org/10.1109/icsccc58608.2023.10176668.

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Liyanarachchi, H. L. C. L., K. K. K. P. Kumara, P. I. Chaminda, W. R. A. Piyumal, Kanishka Yapa, and Shashika Lokuliyana. "Secure Health Management and Prediction System for Chronic Diseases." In 2023 IEEE 7th Conference on Information and Communication Technology (CICT). IEEE, 2023. http://dx.doi.org/10.1109/cict59886.2023.10455158.

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Mohanty, Rupashree, Santosh Kumar Pani, Smriti Nayak, et al. "Comparitive Analysis of Machine Learning Models for Prediction of Fetal Health." In 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). IEEE, 2024. http://dx.doi.org/10.1109/assic60049.2024.10508030.

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G, Chandana, Haritha J, Janani J, Kalpana S, and Vijaya Lakshmi DM. "Chronic Kidney Diseases Prediction Using K-Means Algorithm." In International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/huit4742/icrcct24p141.

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This transformation allows the model to capture the relationships between different health indicators and their correlation with CKD risk. At the core of the predictive analysis lies a Random Forest Classifier, a powerful ensemble learning method known for its accuracy and robustness in classification tasks. The model is trained on a comprehensive dataset encompassing various health metrics associated with CKD. By analysing this data, the classifier predicts the likelihood of a user developing CKD based on their input, enabling early detection and timely intervention. In addition to predicting
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"Changes in Center of Mass during Preliminary Motion for Prediction of Direction Change." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-35.

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Abstract. In recent years, the number of single elderly people has been increasing, and the needs of residents have been diversifying. Towards these backgrounds, we propose the concept of "Biofiled bulding". The aim of Biofied Building is to create living spaces where residents can live safely, securely and comfortably. Small robots are used as an interface between residents and living space in Biofied Building. The aim of using robots is to sense the position and movement of residents in real time and providing feedback to them. However,he present control systems of the robot do not have enou
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Patro, Sibo Prasad, and Neelamadhab Padhy. "A Secure IoT-Cloud Based Remote Health Monitoring for Heart Disease Prediction Using Machine Learning and Deep Learning Techniques." In The 4th International Electronic Conference on Applied Sciences. MDPI, 2023. http://dx.doi.org/10.3390/asec2023-16580.

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