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All Journal International Journal of Electrical and Computer Engineering
Rakhi Khedkar
Sinhgad Academy of Engineering
Computer Science & IT Electrical & Electronics Engineering

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Computer-aided automated detection of kidney disease using supervised learning technique Navaneeth Bhaskar; Priyanka Tupe-Waghmare; Shobha S. Nikam; Rakhi Khedkar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5932-5941

Abstract

In this paper, we propose an efficient home-based system for monitoring chronic kidney disease (CKD). As non-invasive disease identification approaches are gaining popularity nowadays, the proposed system is designed to detect kidney disease from saliva samples. Salivary diagnosis has advanced its popularity over the last few years due to the non-invasive sample collection technique. The use of salivary components to monitor and detect kidney disease is investigated through an experimental investigation. We measured the amount of urea in the saliva sample to detect CKD. Further, this article explains the use of predictive analysis using machine learning techniques and data analytics in remote healthcare management. The proposed health monitoring system classified the samples with an accuracy of 97.1%. With internet facilities available everywhere, this methodology can offer better healthcare services, with real-time decision support in remote monitoring platform.
Co-Authors Navaneeth Bhaskar Priyanka Tupe-Waghmare Shobha S. Nikam