Design of chaotic black hole based feature selection with classification for Chronic Kidney Disease diagnosis

Gokiladevi, M.; kumar, Sundar Santhosh

doi:10.3233/jifs-220994

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The enhanced results of the CKDD-HGSODL approach can be assured by a comparative results analysis, as given in Table 4 and Fig. 14 [15][16]. The outputs depicted the weak outputs of the NN-GA and EP-CRD models.…”

Section: Performance Valiationmentioning

confidence: 89%

“…This method works depends on an ultrasound image that can depicted as a pre-processing stage and the area of kidney interest has been classified from the images of ultrasound. Gokiladevi [15] suggested a new chaotic binary black hole-based FS with a classification method for diagnosing CKD, termed the CBHFSC-CKD approach. This algorithm includes the development of CBH-FS for selecting optimum feature subsets and thus improves the analytic performance.…”

Section: Related Workmentioning

confidence: 99%

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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: Performance Valiationmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

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

2024

IJIES

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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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Design of chaotic black hole based feature selection with classification for Chronic Kidney Disease diagnosis

Cited by 2 publications

References 19 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

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