IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Padhy, Sasmita; Dash, Sachikanta; Routray, Sidheswar; Ahmad, Sultan; Nazeer, Jabeen; Alam, Afroj

doi:10.1155/2022/2389636

Cited by 25 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SHS is made of various computing devices that act proactively on behalf of persistent users [18]. Hence, for making good decisions in SHS, we require essential features, for example, users' preferences need to be considered for nding their choice of interest in certain scenarios [19][20][21][22][23][24]. Here, user preferences deal with the information used for describing the situation of a person considering the physical medical status or requirements.…”

Section: Introductionmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

show abstract

Section: Introductionmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…We need a new noninvasive evaluation model for DN as an early diagnosis method (Zhang et al, 2022). There is a growing need for Internet of Things (IoT)-based mobile medical applications to help predict a disease (Padhy et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice

LIANG¹,

ZHANG²,

YANG³

et al. 2023

Biocell

View full text Add to dashboard Cite

Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice. Then machine learning algorithms, random forest, LASSO logistic regression, and principal component analysis were used to identify key genes (CES1G, CYP4A14, NDUFA4, ABCC4, ACE). Then, the genetic algorithm-optimized backpropagation neural network (GA-BPNN) was used to improve the DN diagnostic model. Results: The AUC value of the GA-BPNN model in the training dataset was 0.83, and the AUC value of the model in the validation dataset was 0.81, while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650, respectively. Thus, this GA-BPNN gave better values than the traditional SVM model. This diagnosis model may aim for personalized diagnosis and treatment of patients with DN. Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4-like 2 (NDUFA4L2) in tubular tissue in DN mice were decreased. Conclusion: The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

show abstract

“…According to the diagnostic objective, backward propagation finds the most appropriate weights for each neuron and subclassifiers as shown in Figure 4 . The cross-entropy loss function [ 35 ] and the Adam optimization algorithm [ 36 ] play an important role in the backward propagation parameters. The former is a widely used loss function in multiclassification tasks, and the latter effectively minimizes the loss function.…”

Section: Intelligent Fault Diagnosis Methods For Rotating Machinery B...mentioning

confidence: 99%

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Cai

Wang

Yao

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.

show abstract

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Cited by 25 publications

References 36 publications

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Contact Info

Product

Resources

About