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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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