Personalized Shatavari Nutrition Recommendations for Menopausal Women using Machine Learning Algorithms

This research provides a comprehensive approach in designing a recommendation system for women’s health issues, with emphasis on menopause and optimal eating plan with Shatavari nutrition plan incorporated. The data includes symptom severity; heart rate; temperature; sleep; and food and water intake, which has been obtained from patients’ medical records, structured surveys, the users themselves, and through healthcare apps. Handling of missing values involves the completion of the missing records by forward method and imputation. Kindly find below the steps taken to pre-process the given data set for its numerical and categorical features respectively; Standard Scaler for numerical data set and OneHotEncoder for the binary data set. Variable creation is used to improve the performances of the models by creating new variables from other variables. Raw, selected and vectorized features are used to perform and compare multiple machine learning algorithms such as linear regression, logistic regression, support vector machines, decision random forest and gradient boosting, XGBoost, LightGBM, and CatBoost. The present study reveals that LightGBM has the maximum accuracy of $96 \%$. The proposed system combines collaborative and content-based filtering, which will provide accurate and individual health and lifestyle recommendations for enhancing the quality of women’s life during menopausal transition.