Detection of Chronic Kidney Disease: A NN-GA-Based Approach

Hore, Sirshendu; Chatterjee, Sankhadeep; Shaw, Rahul Kr.; Dey, Nilanjan; Virmani, Jitendra

doi:10.1007/978-981-10-6747-1_13

Cited by 17 publications

(3 citation statements)

References 14 publications

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“…Right now, health issues progressively encourages the interest of data scientists. In fact, data analytics as a quickly developing zone can be the correct answer for oversee, identify and anticipate illnesses which undermine human life and cause a high monetary expense to health systems [5]. Alaoui et al, (2018) looks to build up a statistical and predictive analysis of an available dataset related to chronic kidney disease (CKD) by utilizing the broadly used software package called IBM SPSS [6] [18].…”

Section: Machine Learning Models and Neural Networkmentioning

confidence: 99%

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Anusha¹,

Vital²,

Sangeeta³

2019

IJRTE

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Chronic kidney disease (CKD) is one of the most widely spread diseases across the world. Mysteriously some of the areas in the world like Srilanka, Nicrgua and Uddanam (India), this disease affect more and it is cause of thousands of deaths particular areas. Now days, the prevention with utilizing statistical analysis and early detection of CKD with utilizing Machine Learning (ML) and Neural Networks (NNs) are the most important topics. In this research work, we collected the data form Uddanam (costal area of srikakulam district, A.P, India) about patient's clinical data, living styles (Habits and culture) and environmental conditions (water, land and etc.) data from 2016 to 2019. In this paper, we conduct the statistical analysis, Machine Learning (ML) and Neural Network application on clinical data set of Uddanam CKD for prevention and early detection of CKD. As per statistical analysis we can prevent the CKD in the Uddanam area. As per ML analysis Naive Bayes model is the best where the process model is constructed within 0.06 seconds and prediction accuracy is 99.9%. In the analysis of NNs, the 9 neurons hidden layer (HL) Artificial Neural Network (ANN) is very accurate than other all models where it performs 100% of accuracy for predicting CKD and it takes the 0.02 seconds process time.

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Section: Machine Learning Models and Neural Networkmentioning

confidence: 99%

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Anusha¹,

Vital²,

Sangeeta³

2019

IJRTE

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“…In the suggested GroupNet, correlated loss (CL) is used to improve classification performance. Hore et al 13 developed a heterogeneous modified artificial neural network (HMANN) on the Internet of Medical Things (IoMT) platform for the early identification, segmentation, and diagnosis of chronic renal failure using ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

An explainable deep learning model for prediction of early‐stage chronic kidney disease

Arumugham,

Sankaralingam,

Jayachandran

et al. 2023

Computational Intelligence

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Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end‐stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early‐stage CKD. The health care applications require interpretability over the predictions of the black‐box model to build conviction towards the model's prediction. Hence, the predictions of the DNN‐CKD model are explained by the local interpretable model‐agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN‐CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN‐CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.

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“…Neural networks and other machine learning techniques have also been applied to identify a patient's stage of chronic kidney disease [22]- [24]. Other studies propose a neural network model for detecting CKD from patient laboratory data [25]- [27], as well as comparisons with other machine learning models [28], [29]. The results of the evaluation metrics have demonstrated the effectiveness of models trained with neural networks, obtaining values of up to 97% accuracy [30].…”

Section: Introductionmentioning

confidence: 99%

Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

et al. 2019

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This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network's prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population. INDEX TERMS Chronic kidney disease prediction, neural networks, case-based reasoning, twin systems, explainable AI, support vector machines, random forest.

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Detection of Chronic Kidney Disease: A NN-GA-Based Approach

Cited by 17 publications

References 14 publications

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

An explainable deep learning model for prediction of early‐stage chronic kidney disease

Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

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