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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.

References

[1]. Amala, S. S., & Mythili, S. (2018). IoT based health care monitoring system for rural pregnant women. International Journal of Pure and Applied Mathematics, 119(15), 837-843.

[2]. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505-1515.

[3]. Devikanniga, D., Ramu, A., & Haldorai, A. (2020). Efficient diagnosis of liver disease using support vector machine optimized with crows search algorithm. EAI Endorsed Transactions on Energy Web, 7(29), e10.

[4]. Dharmireddy, A. K., Ijjada, S. R., Gayathri, K. V., Srilatha, K., Sahithi, K., Sushma, M., & Madhavi, K. (2022, April). Rad-Hard model SOI FinTFET for spacecraft application. In Advances in Micro-Electronics, Embedded Systems and IoT: Proceedings of Sixth International Conference on Microelectronics, Electromagnetics and Telecommunications (ICMEET 2021), 1, 113-119. Singapore: Springer Nature Singapore.

[5]. Dharmireddy, A., & Ijjada, S. R. (2022, April). Design of low voltage-power: Negative capacitance charge plasma FinTFET for AIOT data acquisition blocks. In 2022 International Conference on Breakthrough in Heuristics and Reciprocation of Advanced Technologies (BHARAT) (pp. 144-149). IEEE.

[6]. Dharmireddy, A., Greeshma, M., Chalasani, S., Sriya, S. T., Ratnam, S. B., & Sana, S. (2023, March). Azolla crop growing through IOT by using ARM CORTEX-M0. In 2023 3rd International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-5). IEEE.

[7]. Dharmireddy, A., Manohar, A. S., Hari, G. S., Gayatri, G., Venkateswarlu, A., & Sai, C. T. (2022, June). Detection of COVID-19 from X-RAY images using Artificial Intelligence (AI). In 2022 2nd International Conference on Intelligent Technologies (CONIT) (pp. 1-5). IEEE.

[8]. Ettiyan, R. (2020, December). Survey on IoT based pregnant women health monitoring system. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (pp. 1207-1213). IEEE.

[9]. Evans, W. D., Wallace, J. L., & Snider, J. (2012). Pilot evaluation of the text4baby mobile health program. BMC Public Health, 12, 1-10.

[10]. Fernández-Caramés, T. M., & Fraga-Lamas, P. (2018, November). Design of a fog computing, blockchain and IoT-based continuous glucose monitoring system for crowdsourcing mHealth. In Proceedings, 4(1), 37. MDPI.

[11]. Megalingam, R. K., Boopathi, K., Sarathkumar, K. S., Sreedevi, S., & Vishnu, G. B. (2013, August). Assistive technology for pregnant women health care: Rural area, mobile ultrasound scan system (using ASTM E1384–07 standard). In 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS) (pp. 164-169). IEEE.

[12]. Wang, C. X., Haider, F., Gao, X., You, X. H., Yang, Y., Yuan, D., & Hepsaydir, E. (2014). Cellular architecture and key technologies for 5G wireless communication networks. IEEE Communications Magazine, 52(2), 122-130.

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