A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

Rghioui, Amine; Lloret, Jaime; Sendra, Sandra; Oumnad, Abdelmajid

doi:10.3390/healthcare8030348

Cited by 54 publications

(41 citation statements)

References 20 publications

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“…In [32], an intelligent system consisting of smart devices and sensors, and smartphones for monitoring diabetic patients, by means of machine learning algorithms, is elaborated. The smart system collects data from body sensors and makes diabetes diagnosis using several classification models from supervised machine learning.…”

Section: B Smart Systems In Diabetes Healthcarementioning

confidence: 99%

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

et al. 2021

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working and living environments supporting active and healthy ageing.ABSTRACT A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to healthrelated issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, wellbeing and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.INDEX TERMS T2DM, long-term health risk prediction, machine learning, ensemble learning

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Section: B Smart Systems In Diabetes Healthcarementioning

confidence: 99%

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

et al. 2021

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“…IoT-based Health Monitoring Systems (IoT-HMS) combine the use of sensors, information and communication technologies, generation of massive data, applications of big data algorithms, and artificial intelligence [22] to provide efficient and continuous remote monitoring of patients with realtime notifications. IoT-HMS have the potential to minimize healthcare costs while improving patient care [23] [24] .…”

Section: Introductionmentioning

confidence: 99%

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

et al. 2022

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Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things (IoT) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.

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“…The research showed that by combining feature selection methods with the aforementioned models, Random Forest achieves a better performance in all experimental groups. Rghioui et al [ 19 ] proposed and developed a 5G architecture for continuous monitoring of diabetic patients using machine learning algorithms (Naïve Bayes, ZeroR, OneR, LR, RF and Sequential Minimal Optimization [SMO]) for data classification. Finally, the SMO algorithm exhibited an excellent classification with the highest accuracy of 99.66%, giving a superior classification compared to other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

et al. 2021

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The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.

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A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

Cited by 54 publications

References 20 publications

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

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