Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP

Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri; Poudyal, Ramhari

doi:10.1016/j.measen.2024.101305

Measurement: Sensors

2024

DOI: 10.1016/j.measen.2024.101305

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Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP

Biplov Paneru,

Bishwash Paneru,

Sanjog Chhetri Sapkota

et al.

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Cited by 2 publications

References 26 publications

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Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning

Hoque,

Shrestha,

Sapkota

et al. 2024

Asian J Civ Eng

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Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning

Hoque,

Shrestha,

Sapkota

et al. 2024

Asian J Civ Eng

View full text Add to dashboard Cite

Development of a low-cost IoT-based e-health monitoring system for diabetic patients

Okubanjo,

Alexander,

Olumide

et al. 2024

Journal of Electrical Systems and Inf Technol

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The global need for diabetic patient self-care is increasing. The current global epidemiology of diabetes calls for an improved healthcare management system, particularly in poorer countries, to minimize the burden of diabetes complications and mortality. The main objective of this study seeks to increase diabetic patients’ access to healthcare by utilizing internet of things-based smart self-care high-tech devices. The proposed model was integrated with a Blynk software and hosted in NodeMCU, allowing user to access diabetic health metric from the smartphones or interconnected devices. The e-monitoring system has an inbuilt feedback mechanism that provides online access to healthcare provider for immediate insulin therapy. Furthermore, patients suffering from acute diabetic metabolic issues may have difficulty visiting the hospital. Therefore, they can use the proposed model to track and monitor their blood glucose levels, as well as communicate remotely with their doctor, so improving their health while saving money and time. Also, diabetic patients can use this system to record their health condition on their phone, decreasing the need for doctor visits and clinical medical procedure. The proposed system has several advantages, including speedy internet access and remote monitoring from anywhere in the world. This research also helps to achieve the United Nations’ Sustainable Development Goals, which include good health and well-being, equitable healthcare access, and sustainable cities and communities.

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Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP

Cited by 2 publications

References 26 publications

Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning

Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning

Development of a low-cost IoT-based e-health monitoring system for diabetic patients

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