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This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

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Improved Sampling and Feature Selection to Support Extreme Gradient Boosting for PCOS Diagnosis

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using a patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible that strongly classifies PCOS in patients of different age and conditions.

Related Research Article

M. S. Khan Inan, R. E. Ulfath, F. I. Alam, F. K. Bappee and R. Hasan, "Improved Sampling and Feature Selection to Support Extreme Gradient Boosting For PCOS Diagnosis," 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021, pp. 1046-1050, doi: 10.1109/CCWC51732.2021.9375994.

Lab Members

Rizwan Hasan
Rubaiath E Ulfath

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This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

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