BPSO-PSO-SVM: An Integrated Approach for Cancer Diagnosis

Panigrahi, Amrutanshu; Bhutia, Santosini; Sahu, Bibhuprasad; Galety, Mohammad Gouse; Mohanty, Sachi Nandan

doi:10.1007/978-981-19-2177-3_53

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Instead, pollination is accomplished without the use of insects. Crosspollination happens when pollens from one plant are transferred to another of the same species, whereas self-pollination occurs when pollen is transferred to the same flower or blooms of the same species [30]. Domain optimization defines and incorporates the techniques of biotic pollination, cross-pollination, abiotic pollination, and self-pollination into the flower pollination algorithm.…”

Section: E Flower Pollination Algorithm (Fpa)mentioning

confidence: 99%

En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

Senapati,

Shrivastava,

Mahapatra

2023

IJRITCC

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Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report. Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%.

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Section: E Flower Pollination Algorithm (Fpa)mentioning

confidence: 99%

En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

Senapati,

Shrivastava,

Mahapatra

2023

IJRITCC

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“…The use of computers and computational analysis might be invaluable when compiling a gene signature list for a certain disease. [7]. Microarray data, on the other hand, is often made up of a high number of genes and a small number of samples.…”

Section: Introductionmentioning

confidence: 99%

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

Panigrahi,

Pati,

Sahu

et al. 2023

IEEE Access

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According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for ∼ 10 million fatalities at the end of 2020. The most common types of cancers include Lung, Breast, CNS, Leukemia, Colon, and Cervical Cancer. Early detection of cancer can decrease the death toll. According to the study, if the cancer is identified at its early stage, the death rate can be reduced to ∼85%. In order to reduce the death toll, machine learning (ML) emerges as a significant solution. When it comes to cancer research with ML, biopsy and microarray data come into the front. The biopsy data is less useful as it excludes a patient's genetic information. However, due to the genetic information, the microarray data emerges as a solution to detecting cancer disease. Dealing with microarray data also has some consequences, and high dimensionality is one of them. This article reports an ML-based ensemble model to tackle microarray data issues and provide an effective model for cancer detection. The reported model uses Minimum Redundancy Maximum Relevance (MRMR) as the feature selection algorithm. The Whale Optimization Algorithm (WOA) is implemented for the featured dataset to select the optimistic number of features without affecting microarray data relevance. Then, four ML-based classification models, including Support Vector Machine, Decision Tree, Multi-Layer Perceptron, and Random Forest, are applied as the base learners to make initial predictions. Finally, the voting ensemble technique is applied to the initial prediction to develop an ensemble model for cancer prediction. The proposed En-MinWhale model is evaluated over six different types of cancer microarray datasets, including Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer. Finally, the performance of the proposed model is evaluated using 11 various evaluative parameters, including accuracy, precision, specificity, sensitivity, F-β score, etc. The En-MinWhale model shows 94.09%, 95.83%, 94.86%, 95.00%, 94.85%, and 96.77% accuracy for Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer datasets, respectively, that outperforms other considered hybrid models and can help out the physicians in Cancer diagnosis.

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