A Cloud-Centric Application for Elderly Heart Disease Detection with Machine Learning and Confusion Matrix

Rafly Arief Kanza, M. Udin Harun Al Rasyid, Sritrusta Sukaridhoto, Budi Utomo, Shifa Fauziah, Grezio Arifiyan Primajaya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Elderly individuals often face limited mobility and remote locations, which can hinder their access to specialist healthcare and result in missed diagnoses and poorer health outcomes. This paper addresses this issue by proposing a novel, cloud-centric application for Remote Patient Monitoring (RPM). The goal of this application is to enable elderly patients to track their vital signs from home using Internet of Things (IoT) sensors, leveraging machine learning for analysis. A high-performing Random Forest model is employed to analyze the data, detecting early signs of cardiovascular disease with an accuracy of 82.6%. Doctors can remotely monitor patient health data, which is integrated with electronic health records, facilitating timely follow-up care and personalized treatment recommendations. The method presents a user-centric approach that combines remote self-diagnosis with advanced technology to improve healthcare accessibility for the elderly. The current iteration focuses on heart disease detection, but future developments could expand the application to a broader range of health parameters. It is essential to note that this application serves as a complementary tool to professional medical advice, not a replacement. Clear communication about these limitations within the app is crucial. This research highlights the importance of doctor supervision and professional evaluation in conjunction with self-monitoring through the application.

Original languageEnglish
Title of host publication2024 International Electronics Symposium
Subtitle of host publicationShaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
EditorsAndhik Ampuh Yunanto, Afifah Dwi Ramadhani, Yanuar Risah Prayogi, Putu Agus Mahadi Putra, Weny Mistarika Rahmawati, Muhammad Rizani Rusli, Fitrah Maharani Humaira, Faridatun Nadziroh, Nihayatus Sa'adah, Nailul Muna, Aris Bahari Rizki
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages709-714
Number of pages6
ISBN (Electronic)9798350391992
DOIs
Publication statusPublished - 2024
Event26th International Electronics Symposium, IES 2024 - Denpasar, Indonesia
Duration: 6 Aug 20248 Aug 2024

Publication series

Name2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding

Conference

Conference26th International Electronics Symposium, IES 2024
Country/TerritoryIndonesia
CityDenpasar
Period6/08/248/08/24

Keywords

  • Cloud centric
  • Elderly
  • Heart disease
  • Machine learning
  • Remote patient monitoring

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