International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 07 | July 2022
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
MEDICAL CHATBOT FOR PERINATAL WOMEN USING MACHINE LEARNING Mr. Venkateshwara A1, Aishwarya Rego2, Arpita3, Disha TM4, Raghavnedra B5 1Department
of CSE, BITM Ballari Department of CSE, BITM Ballari ----------------------------------------------------------------------------***-------------------------------------------------------------------------giving them the chance to regulate, manage, maintain, and ABSTRACT – A perinatal woman needs routine 2,3,4Student
avoid likely serious health issues, so providing pregnant women with self-health case services using a machine learning algorithm and a database that stores the mother's days.
examinations and analyses of her medical care. Hospitals are the sole setting where perinatal women can receive medical evaluations, diagnosis, and treatment recommendations. Every woman in the world engages in this. Women consider it to be the most trustworthy method for assessing their health. This technique is being presented as an alternative to going to the hospital and getting a diagnosis from a doctor. The goal of this experiment is to build a chatbot application for pregnant women by combining the concepts of machine learning and natural language processing. Through the series, the perinatal ladies are permitted to interact with a chatbot in a manner similar to how they would with a real person.
The goal of the project is to make it simple for users to communicate with healthcare professionals by responding quickly to their questions. Instead of looking through a list of potentially relevant documents on the web, creating question and answer forums is crucial for responding to those inquiries. Using tongue processing, this method enables computer-human communication (NLP). A chatbot is an entity that imitates human conversation in its specific acceptable setting in conjunction with a written or spoken language using techniques like natural language processing. Semantic understanding uses knowledge of word meaning. The chatbot system communicates with the user by monitoring the status of the conversation and recalling previous requests to provide functionality. They are created using computer algorithms that scan user requests, examine them, and respond to similar queries. The system responds by using a powerful graphical user interface that makes it appear as though a real person is speaking with the user.
KEY WORDS: Medical Chatbot, Perinatal Women, Fetal Health Status.
1. INTRODUCTION When everyone in a society is healthy, both the environment and the people are wealthy. If one wants to be happy, it's necessary to keep their bodies and minds in good shape. According to the most recent news from TOI, women find it time-consuming to undergo daily check-ups at hospitals. We have devised a system that offers perinatal women medical help in order to make this easier. It communicates through a chatbot. The chatbot can be used by perinatal women to discuss their symptoms and difficulties. The chatbot offers the user medical aid after recognising patterns in the user's medical data.
2. LITERATURE SURVEY The authors of [1] are Swanthana Susan Alex, Sandra Varghese, Sera Elsa Joy, and Rohit Binu Methew (2019). In order to develop a chatbot application, this study will combine the ideas of machine learning and natural language processing. Medical chatbot for perinatal women utilising machine learning can be very helpful to people in conducting daily check-ups, making people aware of their health state, and encouraging people to take correct actions to be healthy.
Medical chatbots must answer questions from their users with information based on scientific facts. The foetal health model is used to examine and forecast risk throughout pregnancy for physiological variables including blood pressure, blood glucose level, and weight whose variations during pregnancy might lead to issues that can be diagnosed and prevent subsequent complications generated by these changes. A smart system is created to assist expectant moms at various phases of their pregnancy in light of the aforementioned conditions in developing nations. The suggested hybrid strategy provides risk projections based on values of a collection of physiological indicators received from pregnant women,
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