TY - GEN
T1 - A Cloud-Centric Application for Elderly Heart Disease Detection with Machine Learning and Confusion Matrix
AU - Kanza, Rafly Arief
AU - Al Rasyid, M. Udin Harun
AU - Sukaridhoto, Sritrusta
AU - Utomo, Budi
AU - Fauziah, Shifa
AU - Primajaya, Grezio Arifiyan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Cloud centric
KW - Elderly
KW - Heart disease
KW - Machine learning
KW - Remote patient monitoring
UR - http://www.scopus.com/inward/record.url?scp=85204981043&partnerID=8YFLogxK
U2 - 10.1109/IES63037.2024.10665789
DO - 10.1109/IES63037.2024.10665789
M3 - Conference contribution
AN - SCOPUS:85204981043
T3 - 2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
SP - 709
EP - 714
BT - 2024 International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Ramadhani, Afifah Dwi
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Muhammad Rizani
A2 - Humaira, Fitrah Maharani
A2 - Nadziroh, Faridatun
A2 - Sa'adah, Nihayatus
A2 - Muna, Nailul
A2 - Rizki, Aris Bahari
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Electronics Symposium, IES 2024
Y2 - 6 August 2024 through 8 August 2024
ER -