research-article

Prostate Cancer Prognosis Using Multi-Layer Perceptron and Class Balancing Techniques

Authors:

Manoj KumarAuthors Info & Claims

IC3-2021: Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing

Pages 1 - 6

https://doi.org/10.1145/3474124.3474125

Published: 04 November 2021 Publication History

Abstract

Prostate malignancy is one of the most common malignancies. Early prediction of a cancer diagnosis can upsurge the endurance rate of cancer patients. The advancement of cancer research is boosted with the advent of artificial intelligence. Researchers have developed programmes to aid in cancer detection and prognosis due to the availability of open-source healthcare statistics. Machine Learning (ML) algorithms play a vital role in the field of cancer prognosis. The current study highlights the applications of neural networks to predict prostate cancer. We have accessed prostate cancer records from a publically accessible data repository (Kaggle). Current research work stresses the applications of neural learning approach for cancer prognosis and attaining more accurate prediction outcomes. The study also stresses on the impact of different balancing techniques on imbalanced data. The proposed method enhanced the accurateness from 72% on the imbalanced data to 97% on the oversampled dataset. This study aims to determine whether an artificial neural network (multilayer perceptron, MLP) can accurately predict the diagnosis of prostate cancer. In addition, the experimental results confirm the necessity of data balancing techniques in classification.

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Cited By

Bulusu GVidyasagar KMudigonda MSaikia M(2025)Cancer Detection Using Artificial Intelligence: A Paradigm in Early DiagnosisArchives of Computational Methods in Engineering10.1007/s11831-024-10209-0Online publication date: 8-Jan-2025
https://doi.org/10.1007/s11831-024-10209-0
Kumar ASingh DShankar Yadav R(2024)Class overlap handling methods in imbalanced domain: A comprehensive surveyMultimedia Tools and Applications10.1007/s11042-023-17864-883:23(63243-63290)Online publication date: 11-Jan-2024
https://doi.org/10.1007/s11042-023-17864-8
Akhila KMuthukumaran NAhilan A(2023)Classification of Cervical Cancer Using an Autoencoder and Cascaded Multilayer PerceptronIETE Journal of Research10.1080/03772063.2022.214285970:1(26-36)Online publication date: 14-Feb-2023
https://doi.org/10.1080/03772063.2022.2142859
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IC3-2021: Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing

August 2021

483 pages

ISBN:9781450389204

DOI:10.1145/3474124

Copyright © 2021 ACM.

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Published: 04 November 2021

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IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing

August 5 - 7, 2021

Noida, India

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Cited By

Bulusu GVidyasagar KMudigonda MSaikia M(2025)Cancer Detection Using Artificial Intelligence: A Paradigm in Early DiagnosisArchives of Computational Methods in Engineering10.1007/s11831-024-10209-0Online publication date: 8-Jan-2025
https://doi.org/10.1007/s11831-024-10209-0
Kumar ASingh DShankar Yadav R(2024)Class overlap handling methods in imbalanced domain: A comprehensive surveyMultimedia Tools and Applications10.1007/s11042-023-17864-883:23(63243-63290)Online publication date: 11-Jan-2024
https://doi.org/10.1007/s11042-023-17864-8
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https://doi.org/10.1080/03772063.2022.2142859
Gupta SShabaz MGupta AAlqahtani AAlsubai SOfori I(2023)Personal HealthCare of Things: A novel paradigm and futuristic approachCAAI Transactions on Intelligence Technology10.1049/cit2.12220Online publication date: 6-Apr-2023
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