A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Rabie, Osama; Alghazzawi, Daniyal; Asghar, Junaid; Saddozai, Furqan Khan; Asghar, Muhammad Zubair

doi:10.3389/fpubh.2022.861062

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…In the direction of machine learning, many scholars have established KNN models, LDA models, SVM models, decision trees, and random forests [ 6 , 7 ] to classify and predict diabetes. In the direction of neural networks, Rabie et al used neural networks to predict diabetes symptoms in a Chinese city [ 8 ]. Asghar et al built three supervised learning prediction models to analyse and predict diabetes based on whether the patient has diabetes, including both machine learning methods and neural network methods, including support vector machines (SVM), k-nearest neighbours (k-NNs), and artificial neural networks (ANNs) [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks

Lin

Huang

et al. 2022

Computational Intelligence and Neuroscience

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At present, diabetes is one of the most important chronic noncommunicable diseases, that have threatened human health. By 2020, the number of diabetic patients worldwide has reached 425 million. This amazing number has attracted the great attention of various countries. With the progress of computing technology, many mathematical models and intelligent algorithms have been applied in different fields of health care. 822 subjects were selected in this paper. They were divided into 389 diabetic patients and 423 nondiabetic patients. Each of the subjects included 41 indicators. Too many indicator variables would increase the computational effort and there could be a strong correlation and data redundancy between the data. Therefore, the sample features were first dimensionally reduced to generate seven new features in the new space, retaining up to 99.9% of the valid information from the original data. A diagnostic and classification model for diabetes clinical data based on recurrent neural networks were constructed, and particle swarm optimization (PSO) was introduced to optimise recurrent neural network’s hyperparameters to achieve effective diagnosis and classification of diabetes.

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Section: Introductionmentioning

confidence: 99%

Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks

Lin

Huang

et al. 2022

Computational Intelligence and Neuroscience

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“…The following is a diagram of the CNN layer's constituent elements: The core component of the CNN layer is the sliding of a feature recognition matrix over an input matrix to build a map of the feature matrix (see Figure 3). The feature recognition matrix has unique values that allow significant attributes in the input matrix to be identified [25]. The maxpooling procedure was used to improve the efficiency of the CNN layer by reducing the size of the convolved feature map [26].…”

Section: Cnn Layermentioning

confidence: 99%

Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector

Alzahrani

Asghar

2023

Electronics

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The Internet of Things (IoT) has resulted in substantial advances in the logistics sector, particularly in logistics storage management, communication systems, service quality, and supply chain management. The goal of this study is to create an intelligent supply chain (SC) management system that provides decision support to SC managers in order to achieve effective Internet of Things (IOT)-based logistics. Current research on predicting risks in shipping operations in the logistics sector during natural disasters has produced a variety of unexpected findings utilizing machine learning (ML) algorithms and traditional feature-encoding approaches. This has prompted a variety of concerns regarding the research’s validity. These previous attempts, like many others before them, used deep neural models to gain features without requiring the user to maintain track of all of the sequence information. This paper offers a hybrid deep learning (DL) approach, convolutional neural network (CNN) + bidirectional gating recurrent unit (BiGRU), to lessen the impact of natural disasters on shipping operations by addressing the question, “Can goods be shipped from a source location to a destination?”. The suggested DL methodology is divided into four stages: data collection, de-noising or pre-processing, feature extraction, and prediction. When compared to the baseline work, the proposed CNN + BiGRU achieved an accuracy of up to 94%.

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“…Clinician DSS in diabetes can also assist with risk prediction and diagnosis of diabetes [ 45 ], and subtyping of diabetes into type 1 and type 2 diabetes and rarer but well-established monogenic subtypes [ 46 ]. Type 2 diabetes, however, is a highly heterogeneous condition, and further subtyping into distinct groups based on phenotypic and polygenic clustering may help to predict prognoses and preferential treatment responses; this complexity lends itself well to an AI-driven DSS approach [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

Mackenzie,

Sainsbury,

Wake

2023

Diabetologia

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The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way. Graphical Abstract

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A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Cited by 8 publications

References 27 publications

Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks

Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks

Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

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