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@article{153563,
        author = {Shivani Ramakrishnan and Fouzia Fathima A.M and Jagatheeswari.M},
        title = {Math, Science, and Health: Analysis on Women Health Issues: An Algorithmic Perspective},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {485-488},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153563},
        abstract = {There has been a lack of research and detailed studies on women’s health and diseases that predominantly affect them. In this paper, we aim to examine certain conditions which mainly affect women and identify the factors which play a significant role in their occurrence. For this study, diseases such as Anemia, breast cancer, and  Polycystic ovary syndrome (PCOS) were considered. Machine learning models and algorithms such as XGboost and Random Forest Classifier were used to find top causes.
Model performance and results were examined for each of the diseases, and data interpretation was made with the help of SHAP plots. The results showed that a focused study on women's health yielded less biased and accurate results.
},
        keywords = {Predictive Analysis, Cancer, Anemia, PCOS, Machine Learning},
        month = {},
        }