Machine Learning in Predicting Women’s Health during Pregnancy

Swati Tiwari

Assistant Professor, CS Department

Email: swati.tiwari@kalingauniversity.ac.in

Machine learning can be used to predict women’s health. It can be used to analyze a variety of factors, such as age, lifestyle, medical history, and genetic data, to predict the likelihood of developing certain diseases or conditions, such as breast cancer, heart disease, and diabetes. Machine learning can also be used to identify risk factors for certain conditions and to develop personalized health recommendations for women based on their individual data. Additionally, machine learning can be used to monitor changes in women’s health over time and to provide early warnings of potential health issues. In the context of pregnancy, machine learning can be used to predict the likelihood of preterm birth, low birth weight, and other health issues. Machine learning can also be used to identify factors that are associated with successful pregnancies and to provide personalized health guidance to pregnant women.

Machine  learning can be used in predicting women’s health nursing in various ways. For example, it can be used to analyze a patient’s medical records and identify health risks. It can also be used to build predictive models for diagnosis, treatment, and prognosis of common women’s health issues. Additionally, deep learning can be used to identify patterns in patient data that suggest potential indicators of health risks. Finally, deep learning can be used to develop personalized health solutions and interventions for women’s health nursing.