International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
www.irjet.net
Mom Care: A smart medical app for pregnantwomen O.A.R.P. Dharmadasa Department of Information TechnologySri Lanka Institute of Information Technology Malabe, Sri Lanka it19197906@my.sliit.l k
K.G.P.R. Senevirathna Department of Information TechnologySri Lanka Institute of Information Technology Malabe, Sri Lanka it19964188@my.sliit.l k
H.P.A.S. Thilakarathna Department of Information TechnologySri Lanka Institute of Information Technology Malabe, Sri Lanka it19049946@my.sliit.l k
K.M.T. Pushpamal Department of Information TechnologySri Lanka Institute of Information Technology Malabe, Sri Lanka it19382586@my.sliit.l k
K.B.A.B.Chathurika Academic Coordinator, Matara Centre/Lecturer Sri Lanka Institute of InformationTechnology Matara Centre, Sri Lanka bhagyanie.c@sliit.lk
Laneesha Ruggahakotuwa Assistant Lecturer, Department ofComputer Systems Engineering Sri Lanka Institute of InformationTechnology Malabe, Sri Lanka laneesha.r@sliit.lk
------------------------------------------------------------------------***--------------------------------------------------------------------------management. Many pregnant moms may lack the time or Abstract—Pregnancy is a crucial time in a resources to monitor their health during pregnancy due to the hectic pace of modern life. This is a frequent pregnancy condition that can result in a number of health issues. Hence, a dependable and effective system is required to assist pregnant women in managing their health. The objective of our "Mom care: Smart medical app for pregnant women" project is to answer this demand by delivering a comprehensive and individualized approach to maternity care utilizing machine learning techniques.
woman's life, and it needs routine monitoring of both the mother and the fetus. In this research project, we propose "Mom Care," a smart medical app that employs machine learning algorithms to predict gestational diabetes, monitors fetal health, and give emotional support to expecting women. Mom Care consists of four major components: the gestational diabetes predictor, the smart chatbot, the fetal health predictor, and the emotion detection utilizing facial expressions and chatbot-based treatment. The gestational diabetic predictor employs machine learning algorithms to predict gestational diabetes risk based on clinical and non-clinical data. The smart chatbot is programmed to give 24/7 care to pregnant women, answering their pregnancyrelated questions and offering emotional support. The fetal health predictor forecasts the health of the fetus based on CTG data. Lastly, emotion identification based on facial expressions and treatment with a chatbot employs machine learning algorithms to identify and treat emotional discomfort with a smart chatbot. Our experimental findings indicate that the suggested method can accurately diagnose gestational diabetes, monitor fetal health, and give emotional support to pregnant women, hence enhancing their well-being throughout pregnancy.
Keywords—pregnancy,
prenatal
care,
Recent advancements in machine learning have created new opportunities in healthcare, such as the creation of intelligent medical applications that can assist pregnant women in monitoring their health and well-being. In this research project, we propose "Mom Care," a smart medical app that employs machine learning algorithms to diagnose gestational diabetes, monitors fetal health,24/7 available chatbot and offers emotional support to expecting moms.
machine
learning
I. INTRODUCTION Pregnancy is an exciting and demanding period for expectant women. But it may also be a period of uncertainty, particularly in terms of health and wellness
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Gestational diabetes is a dangerous illness that can have severe effects on both mother and child. The main thing here is when there is gestational diabetes it can leads to type 2 diabetes in future and there is also a chance that the diabetes occurs during pregnancy may be type 2 diabetes [1]. Our project, "Mom care: Smart medical app for pregnant mothers," intends to assist pregnant women in managing their health by predicting gestational diabetes using machine learning techniques. We used the Pima Indians Diabetes Database from kaggle.com to train and test our machine-learning models to accomplish this goal. KNN, SVM, and RF algorithms were used to predict gestational diabetes in pregnant women. We assessed the accuracy of different methods and chose the most accurate one to incorporate into the final machine-learning model for gestational diabetes prediction. By utilizing this approach, pregnant mothers can proactively manage their health and
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