Nowadays machine learning has been increasingly used in health care, prediction, diagnosis, and as a method of determining priority. Here by using the applicability and performance of machine learning methods this model will be used to identify pregnancy complications. The dataset used here consists data of from 1014 patients who have consented to share data such as age, systolic blood pressure, diastolic blood pressure, heart rate, blood glucose levels, body temperature, and finally, the model will predict risk levels or complications in the pregnancy and will classify the patients with a low, medium or high risk of having pregnancy complications. In such a scenario, the pregnancy-related risks can be reduced by forecasting the complications and by taking preventive measures. Thus, the use of predictive modeling became emergent to save the lives of millions of mothers and infant.
anjaliisonii/PregFree-Web_Aap
The main objective is to provide the right care at the right time at the right time to the patient. This personalized patient-centered care will lead to smart resource allocation and patient empowerment.
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