Identification and Analysis of Breast Cancer Disease using Swarm and Evolutionary Algorithm

Shial, Gyanaranjan; Sahoo, Sabita; Panigrahi, Sibarama

doi:10.1109/tensymp54529.2022.9864514

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…The problems of data clustering have been solved by using several conventional techniques; however, these techniques have more chances to converge to local optimal solutions due to the multi-modal nature of the problems. Therefore, to avoid local minima problems, most researchers have solved similar types of multi-modal problems such as time series forecasting [41], the FOPID-based damping controller [27], data-clustering problems [46,47,49], etc. by using natureinspired meta-heuristic algorithms.…”

Section: Methodology For Meta-heuristic Based On Data Clusteringmentioning

confidence: 99%

“…Once an appropriate fitness function is chosen, the clustering algorithm can be converted into an optimization problem that minimizes the intra-cluster distances (compactness) and maximizes the inter-cluster distances (separability). Additionally, a partitional clustering algorithm can handle a large volume of data, which leads to more applications toward the field of research for grouping patterns for example, medical data analysis (for classifying positive and negative symptoms uniquely [46,47]), social network analysis (for identifying fake and real information or users), robotics (for classifying items or humans based on their body shape or activities), and market basket analysis (for classifying consumers according to their purchasing behaviors, etc.). In all of these applications, the nature of data items that are available as patterns are different from each other.…”

Section: Introductionmentioning

confidence: 99%

“…The recent literature reports that most of the meta-heuristic-based optimization algorithms are able to solve real-life optimization problems and those algorithms are such as: grey wolf optimization (GWO) [34], teaching learning-based optimization (TLBO) [44], bacteria foraging optimization (BFO) [17], the whale optimization algorithm (WOA) [33], the JAYA algorithm [43], ant colony optimization (ACO) [10], the sine cosine algorithm (SCA) [32], simulated annealing (SA) [29], the spotted hyena optimization algorithm (SHOA) [9], ant lion optimization (ALO) [31], particle swarm optimizations (PSO) [28], chemical reaction optimization (CRO) [30], differential evolution (DE) [50], etc. In recent times, most researchers have received immense interest in applying such nature-inspired algorithms on clustering problems [46][47][48][49]. Similarly, Jafer and Sivakumar [25] published a review article on a nature-inspired-based meta-heuristic optimization algorithm on ant-based clustering.…”

Section: Introductionmentioning

confidence: 99%

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A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

SHIAL,

Sahoo,

Panigrahi

2023

csci

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This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and three worst omega wolfs of the population are used. So, the best wolfs and worst omega wolfs assist in moving the new solutions towards the best solutions and simultaneously helps in staying away from the worst solutions. This enhances the chances of reaching the near optimal solutions. The superiority of the proposed method is compared with five promising algorithms, namely GWO, Sine-Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), JAYA and K-means algorithms. The result obtained from the Duncan’s multiple range test and Nemenyi hypothesis based statistical test confirms the superiority and robustness of our proposed method.

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Section: Methodology For Meta-heuristic Based On Data Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

SHIAL,

Sahoo,

Panigrahi

2023

csci

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“…To boost the overall effectiveness of the classification model, Sahu and Shrivas (2022) presented a genetic search with the Wrapper Subset Evaluator approach for feature selection [11]. They also classified CKD and non-CKD data using the Bayes Network, Classification and Regression Tree (CART), and J48 classifier.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Optimization Algorithm for Classification of Microarray Datasets with Mayfly and Whale Survival

Ramakrishna,

Rajarajeswari

2023

Int. J. Onl. Eng.

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In the field of bioinformatics, a vast amount of biological data has been generated thanks to the digitalization of high-throughput devices at a reduced cost. Managing such large datasets has become a challenging task for identifying disease-causing genes. Microarray technology enables the simultaneous monitoring of gene expression levels, thereby improving disease diagnosis accuracy for conditions like diabetes, hepatitis, and cancer. As these complex datasets become more accessible, innovative data analytics approaches are necessary to extract meaningful knowledge. Machine learning and data mining techniques can be employed to leverage big and heterogeneous data sources, facilitating biomedical research and healthcare delivery. Data mining has emerged as a vital tool in the medical field, providing insights into illnesses and treatments and enhancing the efficiency of healthcare systems. This thesis aims to present a novel hybrid technique for feature selection using amalgamation wrappers. The proposed approach combines the Mayfly and whale survival strategies, leveraging the strengths of both algorithms. The model was evaluated using various datasets and assessment criteria, including precision, accuracy, recall, F1-score, and specificity. The simulation results demonstrated that the proposed integrated optimization model exhibits improved classification performance with 12% higher accuracy in disease diagnosis.

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“…Popular machine learning algorithm called Decision Tree bases choices on feature values on a tree-like structure [4,5] . The dataset is divided recursively depending on the most useful attributes, creating a decision-making tree [6,7] .…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

K.P.Senthilkumar

2024

jes

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This research focuses on using machine learning to make breast cancer classification better. In this research various machine learning algorithm such as Decision Tree, Linear Discriminant, Support Vector Machine, K-Nearest Neighbors, Probabilistic Neural Network, Logistic Regression, Recurrent Neural Network, and Ensemble Method are used. We tested them using two different ways of splitting the data—90/10 and 70/30—and we also picked important features to consider. The Ensemble Method came in second place with accuracies of 98.2% and 97.6%. The Deep Neural Network performed really well too, with accuracies of 96.2% in the 90/10 split and 89.1% in the 70/30 split. We also found that selecting the right features improved accuracy a lot. This shows how important it is to choose the best features to make the models better. These results show that machine learning can be used to classify breast cancer effectively. The numbers prove that the Deep Neural Network and Ensemble methods have high accuracy, and selecting the right features makes them even better. The research outcomes introduces machine learning techniques that can improve breast cancer diagnosis, potentially changing the way doctors make decisions and improving patient outcomes.

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Identification and Analysis of Breast Cancer Disease using Swarm and Evolutionary Algorithm

Cited by 10 publications

References 19 publications

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

Evolutionary Optimization Algorithm for Classification of Microarray Datasets with Mayfly and Whale Survival

A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification

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