Ovary Cancer Diagnosing Empowered with Machine Learning

Taleb, N, Mehmood, S, Zubair, M, Naseer, I, Mago, B and Nasir, M U (2022) Ovary Cancer Diagnosing Empowered with Machine Learning. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), 16-17 Feb. 2022, Dubai, United Arab Emirates.

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A high mortality rate is associated with ovarian cancer, one of the most common types of cancers in women. Ovarian cancer refers to a group of disorders that develop in the ovaries and spread to the fallopian tubes and peritoneum. Treatment is most effective when ovarian cancer is discovered in its early stages. Machine learning has recently demonstrated that it is capable of better identifying ovarian cancer and its stages. Most modern research studies on ovarian cancer use a single classification model, leading to poor performance in diagnosis. For the detection of ovarian cancer, the highly sophisticated and efficient machine learning algorithms Support vector machine (SVM) and K-Nearest Neighbor (KNN) are employed in this study. Before diagnosing illness, the suggested approach can optimize and standardize data. Experimental results show that SVM has outperformed KNN in both training and validation performance and achieved an accuracy of 98.1% & 97.16% for training and validation respectively. If used in medical diagnosis systems, the proposed model can significantly improve the accuracy of ovarian cancer detection leading to effective treatment and an increase in patient survival rates.

Affiliation: Skyline University College
SUC Author(s): Mago, B ORCID: https://orcid.org/0000-0003-1537-1202
All Author(s): Taleb, N, Mehmood, S, Zubair, M, Naseer, I, Mago, B and Nasir, M U
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: ovary cancer, machine learning,SVM,KNN
Subjects: B Information Technology > BL Machine Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 27 May 2022 14:55
Last Modified: 27 May 2022 14:55
URI: https://research.skylineuniversity.ac.ae/id/eprint/264
Publisher URL: https://doi.org/10.1109/ICBATS54253.2022.9759010
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