Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16495
Title: Optimizing Drug Discovery for Breast Cancer In A Laboratory Environment Using Machine Learning
Authors: Borkhade, Gauri
Singh, Jagendra
Shelke, Nitin Arvind
Upreti, Kamal
Kuwar, Vishakha
Tiwar, Mohit
Keywords: Breast Cancer
Deep Learning
Drug Discovery
Machine Learning Models
Personalized Medicine
Issue Date: 2024
Publisher: 2024 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024
Institute of Electrical and Electronics Engineers Inc.
Citation: pp. 1-5
Abstract: Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including naïve Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. © 2024 IEEE.
URI: https://doi.org/10.1109/WiSPNET61464.2024.10532839
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16495
ISBN: 9798350350845
Appears in Collections:Conference Papers

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