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Proceedings ArticleDOI

Cuckoo search coupled artificial neural network in detection of chronic kidney disease

TL;DR: The experimental results suggest that NN-CS based model is capable of detecting CKD more efficiently than any other existing model.
Abstract: In the present work a Cuckoo Search (CS) trained Neural Network (NN) or NN-CS based model has been proposed to detect Chronic Kidney Disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences of CKD could be unaffordable to many if not detected at an earlier stage. Motivated by this, the NN-CS model has been proposed which significantly overcomes the problem of using local search based learning algorithms to train NNs. The input weight vector of the NN is gradually optimized by using CS to train the NN. The model has been compared with well-known classifiers like Multilayer Perceptron Feedforward Network (MLP-FFN) (trained with scaled conjugate gradient descent) and also with NN supported by Genetic Algorithm (NN-GA). The performance of the classifiers has been measured in terms of accuracy, precision, recall and F-Measure. The experimental results suggest that NN-CS based model is capable of detecting CKD more efficiently than any other existing model.
Citations
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Journal ArticleDOI
TL;DR: Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.
Abstract: At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.

118 citations

Journal ArticleDOI
TL;DR: A neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD) in Colombia and applies and validate a NN-CBR twin system for the explanation of CKD predictions.
Abstract: 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.

68 citations


Cites result from "Cuckoo search coupled artificial ne..."

  • ...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]....

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Proceedings ArticleDOI
01 Aug 2017
TL;DR: Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features, which has been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure.
Abstract: Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

39 citations


Cites methods from "Cuckoo search coupled artificial ne..."

  • ...The experimental setup of NN-GA and NN-PSO is as in [34, 35]....

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  • ...Consequently, meta-heuristic optimization methods, such as SA based methods [21, 22, 23], GA [24, 25], PSO [26, 27], and NSGAII [28, 29] can be employed to train the NN. Generally, several problems involve multiple objectives optimization at the same time to achieve a potent solution The multi-objective optimization [30] can formally be defined by finding the vector [ ]1 2 , , , Tp nx x x x= … of n decision variables such that ( ) ( )1 2( ) , , , ( ) T p nf x f x f x f x= … satisfies some constraints....

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  • ...Table I reports the comparative study of the proposed method with NN-GA and NN-PSO....

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  • ...Consequently, meta-heuristic optimization methods, such as SA based methods [21, 22, 23], GA [24, 25], PSO [26, 27], and NSGAII [28, 29] can be employed to train the NN....

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  • ...A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm)....

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Proceedings ArticleDOI
01 Oct 2017
TL;DR: Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model in terms of testing phase confusion matrix based performance measuring metrics.
Abstract: The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.

36 citations

Journal ArticleDOI
15 Mar 2021
TL;DR: The uncertainty between statistical methods and ML has now been clarified and the study of related research reveals that the prediction of existing forecasting models differs even if the same dataset is used.
Abstract: Data mining (DM) is an instrument of pattern detection and retrieval of knowledge from a large quantity of data. Many robust early detection services and other health-related technologies have developed from clinical and diagnostic evidence in both the DM and healthcare sectors. Artificial Intelligence (AI) is commonly used in the research and health care sectors. Classification or predictive analytics is a key part of AI in machine learning (ML). Present analyses of new predictive models founded on ML methods demonstrate promise in the area of scientific research. Healthcare professionals need accurate predictions of the outcomes of various illnesses that patients suffer from. In addition, timing is another significant aspect that affects clinical choices for precise predictions. In this regard, the authors have reviewed numerous publications in this area in terms of method, algorithms, and performance. This review paper summarized the documentation examined in accordance with approaches, styles, activities, and processes. The analyses and assessment techniques of the selected papers are discussed and an appraisal of the findings is presented to conclude the article. Present statistical models of healthcare remedies have been scientifically reviewed in this article. The uncertainty between statistical methods and ML has now been clarified. The study of related research reveals that the prediction of existing forecasting models differs even if the same dataset is used. Predictive models are also essential, and new approaches need to be improved.

34 citations

References
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Journal ArticleDOI
01 Mar 1996
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
Abstract: Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application.

4,281 citations


"Cuckoo search coupled artificial ne..." refers background in this paper

  • ...For finite number of iterations regardless of the initial weight vector, it is required to attain the optimal weight vector, thus the perceptron learning rule is used [13]....

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Journal ArticleDOI
TL;DR: Experiments show that SCG is considerably faster than BP, CGL, and BFGS, and avoids a time consuming line search.

3,882 citations

Journal ArticleDOI
TL;DR: The data suggest that efforts to reduce mortality in this population should be focused on treatment and prevention of coronary artery disease, congestive heart failure, diabetes mellitus, and anemia.
Abstract: Background Chronic kidney disease is the primary cause of end-stage renal disease in the United States. The purpose of this study was to understand the natural history of chronic kidney disease with regard to progression to renal replacement therapy (transplant or dialysis) and death in a representative patient population. Methods In 1996 we identified 27 998 patients in our health plan who had estimated glomerular filtration rates of less than 90 mL/min per 1.73 m 2 on 2 separate measurements at least 90 days apart. We followed up patients from the index date of the first glomerular filtration rates of less than 90 mL/min per 1.73 m 2 until renal replacement therapy, death, disenrollment from the health plan, or June 30, 2001. We extracted from the computerized medical records the prevalence of the following comorbidities at the index date and end point: hypertension, diabetes mellitus, coronary artery disease, congestive heart failure, hyperlipidemia, and renal anemia. Results Our data showed that the rate of renal replacement therapy over the 5-year observation period was 1.1%, 1.3%, and 19.9%, respectively, for the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (K/DOQI) stages 2, 3, and 4, but that the mortality rate was 19.5%, 24.3%, and 45.7%. Thus, death was far more common than dialysis at all stages. In addition, congestive heart failure, coronary artery disease, diabetes, and anemia were more prevalent in the patients who died but hypertension prevalence was similar across all stages. Conclusion Our data suggest that efforts to reduce mortality in this population should be focused on treatment and prevention of coronary artery disease, congestive heart failure, diabetes mellitus, and anemia.

1,580 citations

Journal ArticleDOI
TL;DR: This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions to apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures.
Abstract: A new metaheuristic optimisation algorithm, called cuckoo search (CS), was developed recently by Yang and Deb (2009). This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions. We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures. The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for further research.

1,339 citations


"Cuckoo search coupled artificial ne..." refers background or methods in this paper

  • ...One of the most efficient meta-heuristic optimization algorithms is the CS [14], which inspired by obligates brood parasitism nature of cuckoo birds....

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  • ...The three descriptive Cuckoo Search algorithm’s rules are as follows [14]:...

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Journal ArticleDOI
TL;DR: In this article, the authors evaluated measures for making comparisons of errors across time series and found that the median absolute error of a given method to that from the random walk forecast is not reliable, and therefore inappropriate for comparing accuracy across series.

1,063 citations