Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique

Kriplani, Himanshu; Patel, B. K.; Roy, Sudipta

doi:10.1007/978-3-030-04061-1_18

Cited by 53 publications

(31 citation statements)

References 11 publications

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“…For this reason, the RKM algorithm is considered to handle these ambiguous objects so that the accuracy of the classification algorithms can be improved. e RKM algorithm is appropriately designed for 8,13,16,20,31,43,53,54,56,73,95,99,111,132,139,144,153,162,156,195,199,206,215,22 Boundary region for cluster 0: 4 14 0, 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29,30,32,33,34,…”

Section: Resultsmentioning

confidence: 99%

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Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Aldhyani

Alshebami

Alzahrani

2020

Journal of Healthcare Engineering

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Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

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Section: Resultsmentioning

confidence: 99%

“…ey have noted that CNN has achieved the best accuracy. Kriplani et al [20] have used deep learning to predict chronic kidney disease. e proposed models are tested by using standard datasets of diseases available on the UCI.…”

Section: Related Studiesmentioning

confidence: 99%

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Aldhyani

Alshebami

Alzahrani

2020

Journal of Healthcare Engineering

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“…By comparing results of all algorithms random forest algorithm has performed better than remaining two algorithms. Himanshu Kriplani et al [19] used deep neural network to predict kidney disease. The CKD dataset is acquired from UCI ML repository.…”

Section: Literature Surveymentioning

confidence: 99%

“…These are the most commonly used machine learning classification techniques. Similarly, the works [12], [14], [19], [20], [21] and [22] contains different types of neural networks for prediction purpose. Some of these works mentioned that the considered neural network has performed better.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Diabetes Correlated Renal Fault Prediction through Deep Learning

Reddy¹,

Sethi²,

Rajender³

2020

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Diabetic nephropathy is one of the complications of diabetes that causes damage to kidneys. Deep learning techniques are widely used to predict different diseases. OBJECTIVES: The main aim of this work is to develop an effective prediction model using deep learning. To get an effective model, a suitable dataset is considered that comprises of features related to diabetic nephropathy. METHODS: Deep belief network (DBN) is the proposed deep learning technique which is compared with naive bayes, CART decision tree, logistic regression and support vector machine. DBN is composed of Restricted Boltzmann Machines (RBM). The algorithms are analysed based on evaluation measures like area under PR curve, area under ROC curve, gini coefficient and jaccard index. RESULTS: After comparison of all algorithms, it was observed that DBN has performed better in terms of AUROC, gini coefficient and jaccard index with values 0.8203, 0.6406 and 0.7777 respectively. But CART obtained better value of 0.9039 only for AUPR. CONCLUSION: The proposed technique has outperformed other techniques in terms of three metrics and is identified as the best performing algorithm. Hence, it is suggested to use DBN while predicting diabetic nephropathy.

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“…This model shows better results as compared to already existing algorithms as it is using the cross-validation technique to avoid over fitting. This model works efficiently provided the disease is detected at an earlier stage [7]. The table 1 shows the various authors' contributions on CKD.…”

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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Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique

Cited by 53 publications

References 11 publications

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Diabetes Correlated Renal Fault Prediction through Deep Learning

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

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