Development of an IoT-Based Smart Health Monitoring System with Heart Attack Prediction Using the SVM (Support Vector Machine) Algorithm
DOI:
https://doi.org/10.62951/ijamc.v2i3.128Keywords:
Algorithm, Heart Attack, IoT, Smart Health Monitoring, Support Vector MachineAbstract
Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.
References
A. Putra and D. P. Rini, “Prediksi Penyakit Jantung dengan Algoritma Klasifikasi,” Annual Research Seminar (ARS), vol. 5, no. 1, pp. 95–99, Feb. 2020 jurnal.unprimdn.ac.id+1jidt.org+1.
S. A. T. Al Azhima et al., “Hybrid Machine Learning Model untuk memprediksi Penyakit Jantung dengan Metode Logistic Regression dan Random Forest,” Jurnal Teknologi Terpadu, vol. 8, no. 1, pp. 40–46, 2022 jurnal.unprimdn.ac.id+1openjournal.unpam.ac.id+1.
H. Hasanah, “Perbandingan Tingkat Akurasi Algoritma Support Vector Machines (SVM) dan C45 dalam Prediksi Penyakit Jantung,” Universitas Nusantara PGRI Kediri, vol. 2, 2023 dspace.uii.ac.id+15ejournal.unma.ac.id+15openjournal.unpam.ac.id+15.
S. N. N. Arif, A. M. Siregar, S. Faisal, dan A. R. Juwita, “Klasifikasi Penyakit Serangan Jantung Menggunakan Metode Machine Learning K Nearest Neighbors (KNN) dan Support Vector Machine (SVM),” Jurnal Media Informatika Budidarma, vol. 8, no. 3, pp. 1617–1626, Jul. 2024 researchgate.net+1jurnal.unprimdn.ac.id+1.
F. Fahrudin, Suroso, dan S. Soim, “Pengembangan Model Support Vector Machine untuk Prediksi Penyakit Jantung menggunakan dataset publik dan statistik pemilihan fitur,” Jurnal Teknologi dan Sistem Informasi (JTSI), 2024 jurnal.unprimdn.ac.id+3openjournal.unpam.ac.id+3jidt.org+3.
Y. Z. Sumarno, S. Sumaryo, dan N. Prihatiningrum, “Desain dan Implementasi Sistem Monitoring Kesehatan Ternak Domba Berdasarkan Suhu Tubuh dan Detak Jantung berbasis IoT,” Jurnal TESLA – Untar, 2022 download.garuda.kemdikbud.go.id+4journal.untar.ac.id+4ejurnal.seminar-id.com+4.
E. Irpanudin et al., “Prediksi Penyakit Jantung Menggunakan Metode Deep Neural Network dengan Memanfaatkan Internet of Things,” Jurnal Informasi dan Teknologi (JIDT), vol. 5, no. 2, pp. 45–55, Jun. 2023 jidt.org.
J. Juhaeriyah, E. Agung, dan R. Wulandari, “Aiming to test the accuracy of the MAX30102 sensor in measuring heart rate and human body temperature,” gartered from Design of Health Monitoring System Based on IoT, 2023 ejurnal.seminar-id.com+5journals.indexcopernicus.com+5ejurnal.seminar-id.com+5.
T. Muthmainnah et al., “Prototipe alat ukur detak jantung menggunakan sensor MAX30102 berbasis Internet of Things (IoT) ESP8266 dan Blynk,” Jurnal Informatika Sunan Kalijaga (JISKA), 2023 researchgate.net+12journal.iistr.org+12journals.indexcopernicus.com+12.
I. Sameh et al., “Sistem Monitoring Pasien Isolasi Mandiri Covid 19 Berbasis Internet of Things dengan sensor MAX30102 dan DHT22 menggunakan ESP32,” Jurnal Teknik Elektro, vol. 10, no. 3, pp. 689–697, 2021 ejournal.unesa.ac.id.
“Sistem Monitoring Detak Jantung dengan Sensor MAX30102 dan DHT22 berbasis IoT,” Teknologi Nusantara, Universitas Islam Nusantara, 2023 citilabs.telkomuniversity.ac.id+7ojs.uninus.ac.id+7repository.unsri.ac.id+7.
N. A. P. Ayu Anggraini, “Sistem Monitoring Detak Jantung dan Suhu Tubuh berbasis IoT menggunakan ESP8266 dan Blynk,” Skripsi, Universitas Sriwijaya, Jan. 2023 citilabs.telkomuniversity.ac.id+3repository.unsri.ac.id+3ejournal.uin-suka.ac.id+3.
I. Izhangghani, I. Hikmah, dan S. Indriyanto, “Prototype of Body Temperature and Oxygen Saturation Monitoring System Using DHT22 and MAX30100 Sensors based on IoT,” Jurnal RESTI, vol. 6, no. 5, Okt. 2022 journal.umy.ac.id+9jurnal.iaii.or.id+9repository.unsri.ac.id+9.
A. Alfarobi, “Patient Bracelet Design for Monitoring Vitalsign SPO2, BPM, Temperature menggunakan MAX30102 dan DHT22 berbasis IoT,” Electromedic, vol. 2, no. 1, pp. 18–28, Jun. 2025 jurnal.ikatemi.or.id+1repository.unsri.ac.id+1.
R. Saragih, “Perancangan dan Analisis Sistem IoT untuk Monitoring Suhu Tubuh berbasis DHT22 dengan Integrasi RFID menggunakan SUS,” PraProposal, Telkom University, Feb. 2024 jurnal.ikatemi.or.id+6citilabs.telkomuniversity.ac.id+6journal.untar.ac.id+6.
“Sistem Monitoring HeartRate dan SpO₂ untuk Pendampingan Real time menggunakan MAX30102 berbasis IoT,” Majalah Teknik, Universitas Muhammadiyah, 2023 ejournal.uin-suka.ac.id+5journal.umy.ac.id+5repository.unsri.ac.id+5.
A. M. Astutik dan R. F. Bakti, “Sistem Monitoring Detak Jantung berbasis LoRa,” E Link: Jurnal Teknik Elektro dan Informatika, vol. 15, no. 1, pp. 19–25, 2020 ejournal.uin-suka.ac.id.
K. Astutik dan R. F. Bakti, “Implementasi Sistem Monitoring Detak Jantung dan Suhu Tubuh menggunakan MQTT,” Jurnal Pengembangan Teknologi dan Ilmu Komputer, vol. 3, no. 11, 2020 ejournal.uin-suka.ac.id+1repository.unsri.ac.id+1.
Hudhajanto, Mulyadi, dan Sandi, “Wearable Sensor Device berbentuk Face Shield untuk Memonitor Detak Jantung berbasis IoT,” Journal of Applied Informatics and Computing, vol. 6, no. 1, pp. 87–92, 2022 jurnal.iaii.or.id+5ejournal.uin-suka.ac.id+5repository.unsri.ac.id+5.
R. Kusuma dkk., “Prototipe Alat Monitoring Kesehatan Jantung berbasis IoT,” Emitor: Jurnal Teknik Elektro, vol. 18, no. 2, pp. 59–63, 2022 ejournal.uin-suka.ac.id+1journal.untar.ac.id+1.
Tiffany Michelle Jeniffer Kulona, Handy Indra Regain Moseya*, Verna Albert Suoth,Pemantauan Suhu Tubuh dan Detak Jantung Berbasis IoT dan Terintegrasi ThingSpeak, SMS dan Telegram, https://ejournal.unsrat.ac.id/v3/index.php/jmuo/index[^45]
Ibrahim Yusuf Zaki,Rancang Bangun Sistem Monitoring Detak Jantung, Suhu Tubuh, dan Cairan Infus Berbasis Internet of Things, Volume 12 No 2 Tahun 2023, 14-22
Gantar Fitra Fahrudin, Suroso, Sopian Soim, Pengembangan Model Support Vector Machine untuk Meningkatkan Akurasi Klasifikasi Diagnosis Penyakit Jantung ,DOI: 10.32493/jtsi.v7i3.42254
Elmi Ramlan Bugis, Amalia C. Nur’Aidha, Dhananjaya Yama Hudha Kumarajati Alat Monitoring Detak Jantung Portable Menggunakan Sensor Max30102 http://dx.doi.org/10.23960/jitet.v12i3.4504[^26]
Implementasi Machine Learning Untuk Prediksi Penyakit Jantung Menggunakan Algoritma Support Vector Machine Rahmat Hidayat, Yandiko Saputra Sy, Teguh Sujana, Mirdatul Husnah, Haris Tri Saputra, Finanta Okmayura ISSN: 2722-0850
Fandi Aziz Pratama, Afu Ichsan Pradana, Dwi Hartanti Pengembangan Aplikasi Mobile Untuk Monitoring Detak Jantung, Saturasi Oksigen Darah, Dan Suhu Tubuh Dengan Integrasi Iot Menggunakan Esp32 P-ISSN : 2460-2108 E-ISSN : 2620-5181
Trisya Septiana, Mona Arif Muda, Deny Budiyanto, Mahendra Pratama, Winaldi Putra Jaya Analisis Penggunaan Support Vector Machine pada Deteksi Dini Penyakit Diabetes Melitus https://doi.org/10.54082/jupin.643[^30]
Risa Adinda Putri1,YeshaDaniela Aedo,Indra Wijaya,Muhammad Roihan Jannatun Adhen,Dicky Pratama Analisis Implementasi Internet Of Thing Dalam Bidang Kesehatan: Systematic Literature Review Vol 1 No 1 (2024): JREIN - Jurnal Rekayasa Informatika
Amanda Iksanul Putri, Yulia Syarif, Puguh Jayadi, Fadlan Arrazak, Febi Nur Salisah Implementasi Algoritma Decision Tree dan Support Vector Machine(SVM)untuk Prediksi Risiko Stuntingpada Keluarga ISSN(P): 2797-2313 | ISSN(E): 2775-8575
Shafira Amanda Putri, Nabilah Selayanti, Mirechelin Kristanaya, Melinda Putri Azzahra, Muhammad Ghinan Navsih, Kartika Maulida Hindrayani Penerapan Machine Learning Algoritma Random Forest Untuk Prediksi Penyakit Jantung https://doi.org/10.33005/senada.v4i1.376[^36]
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