Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

Reddy, Tatiparti B. Prasad; Vydeki, D.

doi:10.1002/cpe.7446

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…In this work, the HGSO algorithm can be designed for selecting an optimal set of features. HGSO is based on the behaviour of Henry's law which is used to evaluate the solubility of lower solubility gas in liquid [18]. Furthermore, the two parameters are temperature and pressure which affects solubility; at high temperature, gases can lesser soluble, but solid becomes more soluble.…”

Section: Fs Using Hgso Algorithmnmentioning

confidence: 99%

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

2024

IJIES

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Chronic Kidney Disease (CKD) is a progressive condition that may cause kidney failure, so earlier diagnosis is critical for proper management. The condition has a large fatality, particularly in developing nations. CKD often remains unnoticed because there is no obvious earlier-stage symptom. Meanwhile, earlier diagnosis and on-time clinical intervention are essential for reducing the disease progression. Detecting CKD using deep learning (DL) methods and feature selection (FS) could be a useful application of artificial intelligence (AI) in healthcare. DL algorithms can provide cost-effective and efficient computer-aided diagnoses (CAD) to assist clinicians in accomplishing earlier CKD recognition. Therefore, this study develops an automated CKD detection using Henry Gas Optimization Algorithm with DL (CKDD-HGSODL) approach. The drive of the CKDD-HGSODL approach is to classify and detect the presence of CKD utilizing FS and hyperparameter tuning strategies. In the presented CKDD-HGSODL technique, min-max scaling can be used to normalize the input data. In addition, the CKDD-HGSODL technique utilizes the HGSO model for selecting optimal features. For the CKD detection process, an attention-based gated recurrent unit (AGRU) model can be utilized. At last, the slime mould algorithm (SMA) can be utilized for the optimal hyperparameter selection of the AGRU approach that aids in improving the classifier results. To validate the outcome of the CKDD-HGSODL approach, a comprehensive simulation value is made on the benchmark CKD database. The obtained performances depict the enhanced detection results of the CKDD-HGSODL approach on CKD diagnosis.

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Section: Fs Using Hgso Algorithmnmentioning

confidence: 99%

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

2024

IJIES

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“…For accurate categorization of CKD and non-CKD, Prasad Reddy and Vydeki [30] suggested the Ebola deep wavelet extreme learning machine (EDWELM). The most discriminative features are selected to increase the effectiveness of categorization using the combination of the darts game and battle royale optimization process known as the darts battle game optimizer.…”

Section: Prediction Of Ckd Is a Popular Study Topic They Applied Mult...mentioning

confidence: 99%

HDLNET: A Hybrid Deep Learning Network Model With Intelligent IOT for Detection and Classification of Chronic Kidney Disease

Venkatrao,

Kareemulla

2023

IEEE Access

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Over 10% of the world's population now suffers from chronic kidney disease (CKD), and millions die yearly. CKD should be detected early to extend the lives of those suffering and lower the cost of therapy. Building such a multimedia-driven model is necessary to detect the illness effectively and accurately before it worsens the situation. It is challenging for doctors to identify the various conditions connected to CKD early to prevent the condition. This study introduces a novel hybrid deep learning network model (HDLNet) for CKD early detection and prediction. A deep learning-based technique called the Deep Separable Convolution Neural Network (DSCNN) has been suggested in this research for the early detection of CKD. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by the Capsule Network (CapsNet). Using the Aquila Optimization Algorithm (AO) method, the pertinent characteristics are selected to speed up the categorization process. The necessary features improve classification effectiveness while needing less computational effort. The DSCNN technique is optimized to diagnose kidney illness as CKD or non-CKD using the Sooty Tern Optimization Algorithm (STOA). The CKD dataset, found in the UCI machine learning repository, is then used to test the dataset. Accuracy, sensitivity, MCC, PPV, FPR, FNR, and specificity are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method.

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An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection

Mamatha,

Terdal

2024

Int Urol Nephrol

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Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

Cited by 9 publications

References 45 publications

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

HDLNET: A Hybrid Deep Learning Network Model With Intelligent IOT for Detection and Classification of Chronic Kidney Disease

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection

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