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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Panigrahi, Amrutanshu
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Computer Science & IT Electrical & Electronics Engineering

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Ant lion and ant colony optimization integrated ensemble machine learning model for effective cancer diagnosis Panda, Pinakshi; Bisoy, Sukant Kishoro; Panigrahi, Amrutanshu; Pati, Abhilash
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp604-613

Abstract

Statistics from reputable sources, including the World Health Organization (WHO), demonstrate that cancer is a leading cause of death globally, accounting for millions of deaths each year. When it comes to the early identification of cancer, machine learning (ML) is crucial. To analyze complex data and identify minute patterns that may indicate the presence of cancer, it employs robust computational approaches. Improving patient outcomes relies on early cancer detection since it paves the way for faster treatment and intervention, which might lead to better prognoses and higher survival rates. To choose features, this study intends to build an ML-based ensemble model utilizing ant colony optimization (ACO) and ant lion optimization (ALO). Next, ML classifiers are used as the initial predictions' basis learners. The last forecast is the result of combining two ensemble methods: voting and averaging classifiers. Four distinct cancer microarray datasets are used to assess the approach. With an accuracy of 99.08% on the Lung cancer dataset, the voting ensemble classifier outperforms the others, according to the empirical analysis.
Breast cancer relapse disease prediction improvements with ensemble learning approaches Sahoo, Ghanashyam; Nayak, Ajit Kumar; Tripathy, Pradyumna Kumar; Pati, Abhilash; Panigrahi, Amrutanshu; Rath, Adyasha; Moharana, Bhimasen
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp335-342

Abstract

Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.
Co-Authors Bisoy, Sukant Kishoro Moharana, Bhimasen Nayak, Ajit Kumar Panda, Pinakshi Pati, Abhilash Rath, Adyasha Sahoo, Ghanashyam Tripathy, Pradyumna Kumar