Dengue Fever Classification Using Gene Expression Data: A PSO Based Artificial Neural Network Approach

Chatterjee, Sankhadeep; Hore, Sirshendu; Dey, Nilanjan; Chakraborty, Sayan; Ashour, Amira S.

doi:10.1007/978-981-10-3156-4_34

Cited by 33 publications

(6 citation statements)

References 16 publications

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“…These files are initial database for overall data processing. Some training datasets have been collected from online databases [42][43][44][45].These databases allow collecting any proteins structures for better experimental analyses. There are various options to collect proteins from online databases ( Figure 2).…”

Section: Methodsmentioning

confidence: 99%

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Kamal

Sarowar

Dey³

et al. 2017

Int. J. Mach. Learn. & Cyber.

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Proteins have a significant role in animals and human health. Interactions among proteins are complex and large. Proteins separations are challenging process in molecular biology. Computational tools help to simulate the analysis in order to reduce the training data into small testing data. Large proteins have been mapped using self-organizing maps (SOMs). Neural network based SOMs has a significant role in reducing the irregular shapes of proteins interactions. Iterative checking enables the organizations of all proteins. In next stage, particle swarm intelligence is applied to classify the proteins' families. In the current work, secondary (Two dimensional) and tertiary proteins (Three dimensional) proteins have been grouped. Two dimensional proteins contain fewer hydrocarbons than three dimensional proteins. For faster analysis, the angles of the proteins are taken into account. The SOMs is compared with Bounding Box approach. In final, the experimental evolutions show that swarm intelligence achieved faster processing through enabling less memory consumptions and time. Since PSO combines proteins datasets in fuzzy values, the compactness or integration of similar proteins are strong. On the other hand, Bounding Box uses the Crisp value. Therefore, it needs more space to organize the whole data. Without SOMs, swarm intelligence also results are poor due to the excessive time consuming and required storage area. Moreover, for almost all classification and clustering tools, it is observed that the overall classification task becomes slow, time consuming, space consuming and also less sensitive because of noises, irrelevant data in input datasets. Thus, the proposed SOM based PSO approach achieved less time consuming with efficient classification into secondary and tertiary proteins.

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Section: Methodsmentioning

confidence: 99%

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Kamal

Sarowar

Dey³

et al. 2017

Int. J. Mach. Learn. & Cyber.

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“…Compared to all other algorithms, POPSO provides a better compromise between sensitivity and specificity when classifying medical datasets. An ANN trained with the PSO algorithm has been used to distinguish dengue hemorrhagic fever (DHF) and dengue fever (DF) patients from patients recovering or not who have Parkinson's disease [45]. Finally, NNPSO was tested with a multi-layer neural network feed-forward network (MLPFFN) classifier to classify dengue fever patients from recovered or non-recovered patients.…”

Section: Related Workmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Data Classification

Bangyal¹,

Nisar

Soomro

et al. 2022

Applied Sciences

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Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.

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“…al. [12] used PSO to select feature for dengue fever classification. Artificial Neural Network (ANN) was used as a classifier in this work.…”

Section: A Nature-inspired Metaheuristic Algorithms For Feature Selectionmentioning

confidence: 99%

Feature Selection Method using Genetic Algorithm for Medical Dataset

Jothi¹,

Husain²,

Rashid³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson's, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer -72.71%, Parkinson's -88.36%, Heart Disease -86.73%, Statlog (Heart) -85.48 %, and Hepatitis -76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient's disease.

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Dengue Fever Classification Using Gene Expression Data: A PSO Based Artificial Neural Network Approach

Cited by 33 publications

References 16 publications

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification

An Improved Particle Swarm Optimization Algorithm for Data Classification

Feature Selection Method using Genetic Algorithm for Medical Dataset

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