Dengue Fever Classification Using Gene Expression Data: A PSO Based Artificial Neural Network Approach
01 Jan 2017-Advances in intelligent systems and computing (Springer, Singapore)-Vol. 516, pp 331-341
TL;DR: A novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF.
Abstract: A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN’s input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system performance was compared to the multilayer perceptron feed-forward neural network (MLP-FFN) classifier. Results proved the dominance of the proposed method with achieved accuracy of 90.91 %.
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01 Jan 2018
50 citations
TL;DR: A deep learning model, namely the Long Short Term Memory (LSTM) network-based patient-dependent model was adopted for FOG detection and a comparison between the proposed model and the traditional machine learning methods, including the linear support vector machine (SVM).
47 citations
TL;DR: A modified bag-of-features method has been proposed to select the most promising genes in the classification process and results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.
Abstract: Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.
43 citations
Cites background or methods from "Dengue Fever Classification Using G..."
...Figure 6 depicts the IFSS plot for precision of MLP-FFN....
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...The proposed model is compared with two different meta-heuristic supported ANN and MLP-FFN classifiers....
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...After each iteration, the MLP-FFN is trained with the reduced dataset having the number of features as in the current iteration....
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...In the current study, the MLP-FFN is used as the base model to find the optimal set of features....
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...The ingenuity of ANN-PSO over MLP-FFN in detecting type dengue fever is further supported by the precision, recall, and Fmeasure....
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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 "Dengue Fever Classification Using G..."
...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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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
References
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1,053 citations
DOI•
01 Jan 1998
TL;DR: A new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA is proposed which combines various features of previous multiobjective EAs in a unique manner and is characterized as follows.
Abstract: Evolutionary algorithms EA have proved to be well suited for optimization prob lems with multiple objectives Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run In this report we propose a new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA It combines various features of previous multiobjective EAs in a unique manner and is characterized as follows a besides the population a set of individuals is maintained which contains the Pareto optimal solutions generated so far b this set is used to evaluate the tness of an individual according to the Pareto dominance relationship c unlike the commonly used tness sharing population diversity is preserved on basis of Pareto dominance rather than distance d a clustering method is incorporated to reduce the Pareto set without destroying its characteristics The proof of principle results on two problems suggest that SPEA is very e ective in sampling from along the entire Pareto optimal front and distributing the generated solutions over the tradeo surface Moreover we compare SPEA with four other multiobjective EAs as well as a single objective EA and a random search method in application to an extended knapsack problem Regarding two complementary quantitative measures SPEA outperforms the other approaches at a wide margin on this test problem Finally a number of suggestions for extension and application of the new algorithm are discussed
788 citations
TL;DR: A particle swarm optimization-based approach to train the NN (NN-PSO), capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistory reinforced concrete building structure in the future.
Abstract: Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.
252 citations
"Dengue Fever Classification Using G..." refers methods in this paper
...Greedy forward selection algorithm [21, 22] has been applied to accomplish this task....
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TL;DR: This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.
Abstract: Background
Dengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.
212 citations
Journal Article•
TL;DR: It is shown that under a certain irrepresentable condition on the design matrix (but independent of the sparse target), the greedy algorithm can select features consistently when the sample size approaches infinity.
Abstract: This paper studies the feature selection problem using a greedy least squares regression algorithm. We show that under a certain irrepresentable condition on the design matrix (but independent of the sparse target), the greedy algorithm can select features consistently when the sample size approaches infinity. The condition is identical to a corresponding condition for Lasso.
Moreover, under a sparse eigenvalue condition, the greedy algorithm can reliably identify features as long as each nonzero coefficient is larger than a constant times the noise level. In comparison, Lasso may require the coefficients to be larger than O(√s) times the noise level in the worst case, where s is the number of nonzero coefficients.
192 citations
"Dengue Fever Classification Using G..." refers methods in this paper
...Greedy forward selection algorithm [21, 22] has been applied to accomplish this task....
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