Private Backend Server Software-Based Telehealthcare Tracking and Monitoring System

Ali, Faten Imad; Ali, Tariq Emad; Al_dahan, Ziad Tarik

doi:10.3991/ijoe.v19i01.32433

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…The future goal is to leverage the approach for African countries to model daily case counts as indicators. Again, surge in publications during and after the COVID-19 pandemic is testament to huge interest in advancing active research trends in multidisciplinary studies [25][26][27] and lessons from the pandemic's impact.…”

Section: Discussionmentioning

confidence: 99%

A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases

Akinola,

Qingguo,

Olukanmi

et al. 2023

Int. J. Onl. Eng.

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In recent years, there has been an increase in studies on time-series forecasting for the future occurrence of disease incidents. Improvements in deep learning approaches offer techniques for modelling long-term temporal relationships. Nonetheless, this design practice is rigorously painstaking, prone to errors, and requires human expertise. The advent of feature enrichment with automatic architecture search typically optimises the discovery of new neural architectures applicable in domains such as time-series modelling. The main methodological contribution of this study is an approach for time-series forecasting using feature-enriched filters and an evolutionary neural architecture search with sequence-to-sequence gated recurrent units (GRU-Seq2Seq). This is applied to the prediction of daily cases of coronavirus disease in South Africa. The highly pathogenic coronavirus pandemic incident data was modelled with filters, optimised hyper-parameter search trials and an evolutional neural algorithm. The proposed model was benchmarked against ARIMA and SARIMA. The model predicted trends for 30, 60 and 90-day horizons and evaluated them for 7, 14 and 31 days. Simulation results demonstrate that observed daily case counts with added filters and evolutionary search optimisation for forecasting improve performance accuracy. Generally, the proposed bFilter+GRU-Seq2Seq with optimal search configuration outperformed ARIMA and SARIMA with lower error scores and higher performance metrics, with an R2 score of 7.48E-01 for a 30-day forecast horizon.

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Section: Discussionmentioning

confidence: 99%

A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases

Akinola,

Qingguo,

Olukanmi

et al. 2023

Int. J. Onl. Eng.

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“…Chronic diseases include diseases such as diabetes, cancer, and heart disease; diabetics have become a prevalent disease, as well as an extremely severe disease that takes many lives annually [2]. Since it is a chronic disease caused by pancreatic failure, frequent and rigorous treatment is essential to avoid damage to different parts such as the veins, nerve cells, and eyelids [3]. The surge in diabetic patients underscores the necessity for advanced technologies to monitor and manage their health effectively [4].…”

Section: Introductionmentioning

confidence: 99%

Diabetic Patient Real-Time Monitoring System Using Machine Learning

Emad Ali,

Imad Ali,

Hussein Morad

et al. 2024

IJCDS

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Continuous monitoring is critical to improving the quality of life of people with diabetes. Leveraging technologies such as the Internet of Things (IoT), modern communication tools, and artificial intelligence (AI) can contribute to reducing healthcare costs. The integration of various communication systems allows the provision of personalized and remote healthcare services. The increasing volume of healthcare data poses challenges in storage and processing. To overcome this challenge, this paper suggests intelligent medical architectures for intelligent e-health applications. To provide cutting-edge medical services, 5G and 6G technologies are necessary, since they can satisfy critical needs, including high bandwidth and energy efficiency. This work presents an intelligent machine learning (ML) using an ensemble learning-based real-time monitoring system for diabetes patients. Mobiles, detectors, and other intelligent gadgets are used as buildings to gather measurements of the body. Subsequently, the collected data undergoes a normalization procedure for preprocessing. Principal Component Analysis (PCA) is employed to extract features. The ranking of every feature in the dataset is then assessed using two feature selection (FS) techniques, namely information gain (IG) and chi-square (chi2), and the association between the features chosen by the FS methods is then found using Pearson's correlation method, which is one of the correlation methods that can be used to find the correlated between the selected features. For diagnostic purposes, the intelligent system employs data classification through an ensemble learning approach using XGBoost and Random Forest (RF) as base models, which is named (ENS XGRF). The final classification is determined by a hard voting mechanism in conjunction with particle swarm optimization (PASWOP). The simulation results underscore the superiority of the suggested approach in terms of accuracy when compared to alternative techniques.

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“…The subject of health monitoring has significantly advanced as a result of breakthroughs in the biological sciences and healthcare field [1]. Many applications have been studied in-depth over the years, including emergencies, home care, hospitals, military, and sports training [2].…”

Section: Introductionmentioning

confidence: 99%

GSM-enabled Wireless Patient Monitoring System Integrating Microcontroller for Managing Vital Signstal signs

Ali,

TEJFEL

et al. 2024

alkej

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The primary focus of this project is developing a cutting-edge remote monitoring of patients device for use in an intensive care unit (ICU). The gadget is designed to capture and transmit vital health data from the patient's body, including oxygen saturation levels, heart rate, and temperatures. It is also capable of monitoring a pacemaker. Information is then sent to a remote location after that. The suggested system receives the patient's vital signs and uses them as input on a computer in the nursing room. This transmission is made possible via a transmitter-receiver system consisting of two Arduino kits connected to Bluetooth devices. If any anomalous numbers are discovered as the data is processed, the computer notifies the doctor via the Global System for Mobile Communications (GSM) and the doctor's mobile device. The primary objective of this device is to enable remote monitoring of a patient's medical parameters and prompt alerting of doctors who are situated in remote places of any concerning circumstances. The doctor's mobile device receives the collected values from the workstation. The entire system's structure is implemented using the Visual Basic programming dialect.

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Private Backend Server Software-Based Telehealthcare Tracking and Monitoring System

Cited by 14 publications

References 22 publications

A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases

A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases

Diabetic Patient Real-Time Monitoring System Using Machine Learning

GSM-enabled Wireless Patient Monitoring System Integrating Microcontroller for Managing Vital Signstal signs

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