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- W4313289225 abstract "Polyfolicular ovarian syndrome or (PCOS) is one of a medical disorder that affects female pregnancy. This paper proposes a method of prediction of PCOS using an Artificial Neural Network (ANN). These are disorders that occur in female ovaries which will lead to infertility. A lot of factors that will be considered to predict whether a woman comes under PCOS or not are obesity, hair distribution, menstrual abnormalities (BMI), Follicle-stimulating hormone, Luteinizing hormone), e.t.c. The data set considered for analysis consists of 44 features for detecting PCOS but the problem here with the data is its imbalance. A more significant number of PCOS fewer patient samples are contributed to the dataset to overcome a proposed method is used in imbalanced learning techniques such as oversampling minority data, under-sampling majority data, SMOTE, and ensemble. The features are extracted from the data set and classified with an ANN-based algorithm. The scaling technique is done on the data to have the same range and fed into the network for training and testing. Data visualization is done to find the correlation between the data." @default.
- W4313289225 created "2023-01-06" @default.
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- W4313289225 date "2022-09-21" @default.
- W4313289225 modified "2023-09-27" @default.
- W4313289225 title "Review of Artificial Intelligent based Algorithm for Prediction of Polycystic Ovary Syndrome(PCOS) from Blood Samples" @default.
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- W4313289225 doi "https://doi.org/10.1109/icirca54612.2022.9985699" @default.
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