Research Article
Implementation of Fuzzy C-Means (FCM) Method For Grouping Heart Disorder Patient Data
@INPROCEEDINGS{10.4108/eai.11-7-2019.2298039, author={Sumiati Sumiati and Yoti Marelita and Akip Suhendar and Riyan Naufal Hay’s and Haris Triono Sigit and Ahmad Dedi Jubaedi}, title={Implementation of Fuzzy C-Means (FCM) Method For Grouping Heart Disorder Patient Data}, proceedings={Selected Papers from the 1st International Conference on Islam, Science and Technology, ICONISTECH-1 2019, 11-12 July 2019, Bandung, Indonesia}, publisher={EAI}, proceedings_a={ICONISTECH-1}, year={2020}, month={11}, keywords={fuzzy c-means clustering heart disease electrocardiogram bipolar waves}, doi={10.4108/eai.11-7-2019.2298039} }- Sumiati Sumiati
Yoti Marelita
Akip Suhendar
Riyan Naufal Hay’s
Haris Triono Sigit
Ahmad Dedi Jubaedi
Year: 2020
Implementation of Fuzzy C-Means (FCM) Method For Grouping Heart Disorder Patient Data
ICONISTECH-1
EAI
DOI: 10.4108/eai.11-7-2019.2298039
Abstract
Heart disease is a fatal disease for human health, resulting in death. Heart disease is a non-communicable disease, but the biggest contributor to mortality, in the amount of 37% (Indonesia Heart Foundation, 2016). The problems are there no system that is able to read the result of electrocardiogram (ECG), so the ECG result still require a doctor to read the result of electrocardiogram and there is no system that can grouping patients with cardiac abnormalities. This research aims to clustering data on patients with cardiac abnormalities with variable age, heart rate (HR) and bipolar waves (QRS, PQ, QT and QTc) using the Fuzzy C-Means approach. The sample data used in this research were 30 patients. The result of this research indicate that the clustering process stops at 17th iteration with objective function value 1033148,1702, there are 2 patients entered into cluster 1 and 28 patients entered into cluster 2.
