TY - GEN
T1 - Early diagnosis of polycystic ovarian syndrome (PCOS) using machine learning
T2 - 2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023
AU - Krishana, Shivam
AU - Sharma, Sparsh
AU - Singh, Saurabh
AU - Yoon, Byungun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In recent years, women's reproductive health has become a significant concern. Issues like preterm abortions, infertility, ovarian disorders, and declining fertility rates are prevalent. Polycystic Ovarian Syndrome (PCOS), a common reproductive disorder, often leads to infertility due to irregular cycles and elevated androgens. Despite its unclear cause and cure, early detection and intervention are vital. Researchers are exploring AI-based diagnostics to expedite diagnosis and mitigate clinical challenges. Our methodology revolves around leveraging non-invasive parameters to construct a feature vector optimized for machine learning algorithms. We utilize Principal Component Analysis (PCA) as a crucial step for dimensionality reduction, which streamlines the dataset's representation while retaining vital information. To bolster the accuracy of PCOS diagnosis, we deploy a majority voting ensemble model that incorporates five base models. This ensemble approach not only enhances classification precision but also addresses issues like overfitting and model robustness, making it especially valuable when dealing with datasets of limited size. The accuracy, precision, f1-score, and recall for the suggested model are found to be 84.3%, 76.1%, 81%, and 84.3% respectively. Our research shows that our ensemble model performs better overall and across individual classes than the fundamental models. This development represents a substantial advancement in the field of PCOS diagnosis, showcasing the pivotal role of machine learning in enhancing diagnostic precision.
AB - In recent years, women's reproductive health has become a significant concern. Issues like preterm abortions, infertility, ovarian disorders, and declining fertility rates are prevalent. Polycystic Ovarian Syndrome (PCOS), a common reproductive disorder, often leads to infertility due to irregular cycles and elevated androgens. Despite its unclear cause and cure, early detection and intervention are vital. Researchers are exploring AI-based diagnostics to expedite diagnosis and mitigate clinical challenges. Our methodology revolves around leveraging non-invasive parameters to construct a feature vector optimized for machine learning algorithms. We utilize Principal Component Analysis (PCA) as a crucial step for dimensionality reduction, which streamlines the dataset's representation while retaining vital information. To bolster the accuracy of PCOS diagnosis, we deploy a majority voting ensemble model that incorporates five base models. This ensemble approach not only enhances classification precision but also addresses issues like overfitting and model robustness, making it especially valuable when dealing with datasets of limited size. The accuracy, precision, f1-score, and recall for the suggested model are found to be 84.3%, 76.1%, 81%, and 84.3% respectively. Our research shows that our ensemble model performs better overall and across individual classes than the fundamental models. This development represents a substantial advancement in the field of PCOS diagnosis, showcasing the pivotal role of machine learning in enhancing diagnostic precision.
KW - ensemble learning
KW - polycystic ovarian syndrome
KW - principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=85190103912&partnerID=8YFLogxK
U2 - 10.1109/MoSICom59118.2023.10458835
DO - 10.1109/MoSICom59118.2023.10458835
M3 - Conference contribution
AN - SCOPUS:85190103912
T3 - Proceedings of IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023
SP - 307
EP - 312
BT - Proceedings of IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023
A2 - Nayak, Jagadish
A2 - Gaidhane, Vilas H
A2 - Goel, Nilesh
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 December 2023 through 9 December 2023
ER -