Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning
- Title
- Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning
- Creator
- Borkhade G.; Singh J.; Shelke N.A.; Upreti K.; Kuwar V.; Tiwar M.
- Description
- 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 nae 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.
- Source
- 2024 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Breast cancer; Deep Learning; Drug discovery; Machine learning models; Personalized medicine
- Coverage
- Borkhade G., Bharati Vidyapeeth College of Engineering, Navi Mumbai, India; Singh J., Alliance School of Advanced Computing, Alliance University, Bangalore, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Upreti K., CHRIST (Deemed to Be University), Delhi-NCR, Department of Computer Science, Ghaziabad, India; Kuwar V., Dr. D y Patil Vidyapeeth, Centre for Online Learning, Pune, India; Tiwar M., Bharati Vidyapeeth's College of Engineering, Computer Science and Engineering, Delhi, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035084-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Borkhade G.; Singh J.; Shelke N.A.; Upreti K.; Kuwar V.; Tiwar M., “Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19433.