An Approach for Analysis and Prediction of CKD using Deep Learning Architecture

Bhaskar, Navaneeth; Suchetha, M.

doi:10.1109/icces45898.2019.9002214

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…Table 7 provides a clear comparison of the many cutting-edge deep-learning models used to study and predict kidney disorders, along with their results achieved. In recent years, various deep learning techniques such as ANN; Bi-LSTM and auto-encoders; feedforward neural networks, CNN-SVM; CNN _ Softmax have been used for kidney disease prediction (Almansour et al 2019, Kriplani et al 2019, Ren et al 2019, Bhaskar and Suchetha 2019, Dohare and Sachdeva 2020, Gupta et al 2020. Some use the CKD dataset acquired from the UCI Machine Learning Repository, whereas others work on the ORDBA (standard-based) dataset.…”

Section: Discussionmentioning

confidence: 99%

Standard-based personalized healthcare delivery for kidney illness using deep learning

Sachdeva

2023

Physiol. Meas.

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Motivation and Objective: Predictive analytics is one of the activeareas of research in healthcare. It aims to provide better services tothe patient and helps the medical practitioners to know what particulartreatment a patient may need based on their past data. Deep learning isan emerging branch of machine learning in which deep artificial neuralnetworks are used to learn a specific pattern for mapping input to output.It has revolutionized predictive analytics by achieving far better accuracythan conventional learning models. This paper aims to analyze the effectof deep learning on standardized Electronic Health Records dataset bydiagnosing kidney-related diseases.Approach: The current study uses a general modularized deep learningarchitecture called Encoder-Combiner-Decoder (ECD), which offersa robust framework. The model’s performance is enhanced by theavailability of variations and extensions to the basic ECD architecturecorresponding to respective input and output feature types. TheopenEHR Benchmark Dataset (ORBDA) dataset is used to train themodel. It is a real-world dataset that has been provided by the BrazilianPublic Health System through the SUS (DATASUS) DatabaseDepartment of Informatics.Results: In the current research, the model trained using deep learningon the part of this benchmark dataset can help in diagnosing kidneyrelatedillnesses.The evaluation metrics show high precision, recall, andF1 score for kidney-related disease, representing that they can beidentified almost every time.Significance: The model is a novel attempt on analyzing a standardizedhealthcare dataset that can be deployed in medical institutions toevaluate its performance by a medical professional.

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Section: Discussionmentioning

confidence: 99%

Standard-based personalized healthcare delivery for kidney illness using deep learning

Sachdeva

2023

Physiol. Meas.

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“…They are made up of several layers, including fully connected, pooling and convolutional layers. CNNs can be used for extracting features because they can automatically learn hierarchical representations of features from the input data [13]. The first layer in the CNN module is the convolution layer.…”

Section: Module For Extracting Featuresmentioning

confidence: 99%

An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications

Bhaskar,

Ajithkumar,

Tupe-Waghmare

2024

IJECE

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In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.

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“…The core of the CNN layout is the convolution layer. Using kernels, this layer extracts the important input features [18]. Two functions are involved in the mathematical process known as convolution.…”

Section: Deep Learning Prediction Modelmentioning

confidence: 99%

CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease

Bhaskar,

Borhade,

Barekar

et al. 2024

IJEECS

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An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach.

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An Approach for Analysis and Prediction of CKD using Deep Learning Architecture

Cited by 14 publications

References 27 publications

Standard-based personalized healthcare delivery for kidney illness using deep learning

Standard-based personalized healthcare delivery for kidney illness using deep learning

An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications

CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease

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