i-manager Publications

Suggestion of Tablets with Machine Learning and IoT for Women

Swathi V.*, Ajay Kumar Dharmireddy**, Ravi Kumar M.***, Veera Kumar B.****, Sai Prasad P.*****

*-***** Department of Electronics and Communication Engineering, Sir C.R.Reddy College of Engineering, Eluru, Andhra Pradesh, India.

Periodicity:July - December'2024

Abstract

In developing nations, much of the population resides in rural regions where medical systems lack integration for information exchange. As a result, pregnant women frequently cannot consult physicians throughout their pregnancies. The proposed study aims to develop a Smart and Intelligent Medicine Recommendation and IoT Monitoring System using Raspberry Pi. This system integrates advanced health monitoring technologies, including the MAX30102 sensor for heart rate, temperature, and SpO2 measurements. Through an interactive method, the system gathers additional input details from the user. It is designed to differentiate between pregnant and non-pregnant women, providing personalized medicine recommendations accordingly. An accelerometer sensor detects fetal movement, confirming pregnancy when the interactive method is inconclusive. The temperature, heart rate, accelerometer data, and pregnancy confirmation are displayed in ThingView and on a mobile phone. This system is sensitive and lightweight, making it ideal for home monitoring. While ultrasonography is currently used, it has limitations, such as prolonged duration and high cost. Furthermore, the limitations of ultrasound scanning for fetal subjects need to be better understood, and ultrasound scans are not always recommended.

Keywords

Chronic Kidney Disease (CKD), Machine Learning, Random Forest, KNN, Accuracy.

How to Cite this Article?

Swathi, V., Dharmireddy, A. K., Kumar, M. R., Kumar, B. V., and Prasad, P. S. (2024). Suggestion of Tablets with Machine Learning and IoT for Women. i-manager’s Journal on IoT and Smart Automation, 2(2), 13-20.

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