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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Journal of Engineering and Scientific Research (JESR)
Sigwele, Tshiamo
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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ENERGY EFFICIENT HEALTHCARE MONITORING SYSTEM USING 5G TASK OFFLOADING Sigwele, Tshiamo; Naveed, Arjmand; Hu, Yim-Fun; Ali, Muhammad; Susanto, Misfa
Journal of Engineering and Scientific Research Vol 1, No 2 (2019)
Publisher : University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v1i2.12

Abstract

Healthcare expenses can be significantly reduced, and lives saved by enabling the continuous monitoring of patient health remotely using Wireless Body Sensor Networks (WBSN). However, an energy efficient mobile gateway (e.g. 5G smartphone) is required which moves with the patient in real time to process the data from the bio sensors without depleting the battery. This paper proposes a 5G based healthcare cardiovascular disease REmote Monitoring system called 5GREM using Electrocardiogram (ECG) bio sensor as a BSN device. The aim is to monitor and analyse the patient?s heart rhythms and send emergency alerts during irregularities to the nearest caregivers, ambulance or physician to minimise heart attacks and heart failures while saving energy. Since ECG signal execution is computer intensive, requests from the ECG sensor are either executed locally on the gateway, offloaded to nearby mobile devices or to the 5G edge while considering the battery level, CPU level, transmission power, delays and task fail rate
Interoperability in healthcare: a critical review of ontology approaches and tools for building prescription frameworks Okon, Eunice Chinatu; Sigwele, Tshiamo; Galani, Malatsi; Mokgetse, Tshepiso; Hlomani, Hlomani
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp366-381

Abstract

Efficient healthcare interoperability is pivotal for delivering high-quality patient care. This research article presents a critical review of ontology based approaches and tools in the development of ontology-based electronic prescriptions (e-prescription), with a focus on enhancing healthcare interoperability. The investigation encompasses two major domains: ontology overview and healthcare interoperability using semantic e-prescription. In the ontology overview, we scrutinize various aspects of ontology development, including the methodologies, languages, tools, and evaluation metrics adopted from literature. Notable comparisons between ontologies and databases are explored. Additionally, we delve into the challenges associated with ontology development and provide a comprehensive summary of methodologies, languages, tools, and evaluation approaches. Healthcare interoperability using semantic e-prescription undertakes a detailed review of e-prescription systems, emphasizing their critical role in healthcare interoperability. A thorough examination of frameworks facilitating semantic e-prescription is presented, offering a nuanced perspective on their contributions and limitations. The section concludes with a concise summary of the key findings from the e-prescription framework review. The article further addresses challenges in healthcare interoperability, including data standardization and system integration issues. To direct continuing research efforts that integrate cutting-edge technologies and interdisciplinary collaborations, future directions and emerging trends are outlined.
Optimizing diabetes prediction using machine learning: a random forest approach Maenge, Aone; Sigwele, Tshiamo; Bhende, Cliford; Mokgethi, Chandapiwa; Kuthadi, Venumadhav; Omogbehin, Blessing
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp454-468

Abstract

Diabetes, a leading cause of global mortality, is responsible for millions of deaths annually due to complications such as heart disease, kidney failure, and stroke. Projections indicate that 700 million people will be affected by diabetes in 2045, placing immense strain on global healthcare systems. Early detection and accurate prediction of diabetes are essential in mitigating complications and reducing mortality rates. However, existing diabetes prediction frameworks face challenges, including imbalanced datasets, overfitting, inadequate feature selection, insufficient hyperparameter tuning, and lack of comprehensive evaluation metrics. To address these challenges, the proposed random forest diabetes prediction (Random DIP) framework integrates advanced techniques such as hyperparameter tuning, balanced training, and optimized feature selection using a random search cross-validation (RandomizedSearchCV). This framework significantly improves predictive accuracy and ensures reliable clinical applicability. Random DIP achieves 99.4% accuracy, outperforming related works by 7.23%, the area under curve (AUC) of 99.6%, surpassing comparable frameworks by 7.32%, a recall of 100%, exceeding existing models by 9.65%, a precision (97.8%), F1-score (98.9%), and outperformance of 6.69%. These metrics demonstrate Random DIP's excellent capacity to identify diabetes cases while minimizing false negatives (FPs) and providing reliable predictions for clinical use. Future work will focus on integrating real-time clinical data and expanding the framework to accommodate multi-disease prediction for broader healthcare applications.
A review on ischemic heart disease prediction frameworks using machine learning Bhende, Kabo Clifford; Sigwele, Tshiamo; Mokgethi, Chandapiwa; Maenge, Aone; Kuthadi, Venu Madhav
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp361-372

Abstract

Ischemic heart disease (IHD) is a leading cause of mortality worldwide, calling for advanced predictive models for timely intervention. Current literature reviews on machine learning (ML)-based IHD prediction frameworks often focus on predictive accuracy but lack depth in areas like dataset diversity, model interpretability, and privacy considerations. Existing IHD prediction frameworks face limitations, including reliance on small, homogenous datasets, limited critical analysis, and issues with model transparency, reducing their clinical utility. This review addresses these gaps through a systematic, comparative analysis of popular ML models, such as random forest (RF) and support vector machines (SVM), noting their strengths and limitations. Key contributions include a qualitative examination of prevalent tools, datasets, and evaluation metrics, identification of gaps in dataset diversity and interpretability; and recommendations for improving model transparency and data privacy. Major findings reveal a trend toward ensemble models for accuracy but highlight the need for explainable artificial intelligence (AI) to support clinical decisions. Future directions include using federated learning to enhance data privacy, integrating unstructured data for comprehensive prediction, and advancing explainable AI to build trust among healthcare providers. By addressing these areas, this review aims to guide future research toward developing robust, transparent ML frameworks that can be more effectively deployed in clinical settings.
SNR Gain Evaluation in Narrowband IoT Uplink Data Transmission with Repetition Increment: A Simulation Approach Hamdani, Fadil; Susanto, Misfa; Sigwele, Tshiamo
Journal of Engineering and Scientific Research Vol. 5 No. 1 (2023)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v5i1.160

Abstract

Deploying Internet of Things (IoT) on a large scale necessitates widespread network infrastructures supporting Machine Type Communication. Integrating IoT into cellular networks like LTE, known as Narrowband-IoT (NB-IoT), can fulfill this infrastructure need. Standard 3GPP Release 13 introduces NB-IoT's Repetition features, expanding radio transmission coverage while maintaining LTE performance. Focusing on uplink data traffic, this study examines NB-IoT's repetition mechanism, grid resource distribution, and NPUSCH performance through simulations. Results show that at SNR greater than -5 dB, maximum repetitions of 128 yield the highest BLER, while minimum repetitions of 2 result in the lowest. Quadrupling repetitions increases SNR by 5 dB, emphasizing repetition's role in error mitigation and uplink reliability, especially in challenging SNR conditions. For optimal throughput in SNR above -5 dB, maximum repetitions of 128 for NPUSCH format 1 are recommended. These findings underscore the importance of repetition in enhancing Narrowband IoT performance, offering insights for system optimization, where increasing the number of repetitions generally leads to higher SNR gain. The attained BLER and throughput values from Narrowband IoT simulations highlight the robustness of data transmission across varying channel conditions, affirming NB-IoT applicability to a wide range of IoT applications.
Co-Authors Bhende, Cliford Bhende, Kabo Clifford Galani, Malatsi Hamdani, Fadil Hlomani, Hlomani Hu, Yim-Fun Kuthadi, Venu Madhav Kuthadi, Venumadhav Maenge, Aone Misfa Susanto Mokgethi, Chandapiwa Mokgetse, Tshepiso MUHAMMAD ALI Naveed, Arjmand Okon, Eunice Chinatu Omogbehin, Blessing