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Rusydi Umar
Universitas Ahmad Dahlan
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Journal : scientific journal of informatics

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Heart Disease Clustering Modeling Using a Combination of the K-Means Clustering Algorithm and the Elbow Method Wala, Jihan; Herman, Herman; Umar, Rusydi; Suwanti, Suwanti
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14096

Abstract

Purpose: Heart disease is the leading cause of death throughout the world, especially in developing countries like Indonesia. Modern approaches for diagnosing and managing heart disease rely on machine learning due to the complexity of medical data. Among the biggest challenges in using machine learning is clustering heart disease patients. This study aims to develop a machine-learning model using K-means clustering to determine the severity and level of patient emergencies. The specific objective of the model is to find the optimal number of clusters using the Elbow Method. Methods/Study design/approach: Model development using a dataset from the Kaggle Repository consisting of 303 patient data. Each data point consists of the attributes age, gender, type of chest pain, blood pressure, serum cholesterol level, blood sugar, electrocardiography results, maximum heart rate, angina, ST depression, and segment slope ST. The combination of the K-means clustering algorithm and the Elbow Method is expected to find the optimal number of clusters in the developed model. Result/Findings: The results of building a machine learning model show that k-means clustering is quite effective in clustering heart disease patients. For the model built with 303 data points, the elbow method successfully identified the optimal number of clusters, resulting in two clusters (k=2), where the elbow point on the graph shows a significant decrease in the Sum of Squared Errors (SSE). Novelty/Originality/Value: This study combines the k-means clustering algorithm and the elbow method to determine the severity and level of patient emergencies. The clustering model produces specific risk clusters that help doctors determine more appropriate interventions.
Co-Authors Aang Anwarudin Abdul Fadlil Abdul Fadlil Abdul Fadlil Abdul Hadi Abdullah Hanif Abe, Tuska Achmad Dito Achmad Nugrahantoro Achmad Ridwan Agus Prasetyo Marsaid Ahmad Fajar Sidiq Ahmad Ikrom Ahnafi, Muhammad Ghazi Ahwan Ahmadi Aini, Fadhilah Dhinur Aji Nugroho Akbar, Zulfikri Alameka, Faza Alameka, Faza Aldi Rifki Andriawan Alfiansyah Imanda Putra Alfiansyah Imanda Putra Alfian Alwas Muis Anak Agung Gede Sugianthara Andhy Sulistyo Andi Zulherry . Anggara, Rio Anton Yudhana Anton Yudhana Anwarudin, Aang Ardi Pujiyanta Ardiansyah, Ricy Arfiani Nur Khusna Arief Setyo Nugroho Arief Setyo Nugroho Arief Setyo Nugroho Arief Setyo Nugroho Arif Budiman Arif Budiman Arif Wirawan Muhammad Arizona Firdonsyah Asruddin Asy’am, Muhammad Hasfar Aulyah Zakilah Ifani Az-Zahra, Rifqi Rahmatika Bashor Fauzan Muthohirin Bella Okta Sari Miranda Bintang, Rauhulloh Noor Busthomi, Iqbal Bustomi, Iqbal Dani Wahyudi Deni Kuswandani Deni Murdiani Dewi Astria Faroek Dewi Estri Jayanti Dewi Estri Jayanti Dewi Sahara Dewi Sahara Nasution Dewi Soyusiawaty Dwi Susanto Dzakwan, M Sabiq Eko Handoyo Elfaditiya Wardaya Ermin Ermin Fadhilah Dhinur Aini Fadlil, Abdu Fadlillah Mukti Ayudewi Fahmi Anwar Faiz Isnan Abdurrachman Faizin Ridho Fathia Irbati Ammatulloh Fauzan, Fauzan Faza Alameka Faza Alameka Fijaya Dwi Bimasakti Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Yasin Fitrah Juliansyah Fitrah Juliansyah Gema Kharismajati Gustafi, Muhammad Fauzan Gustafi, Muhammad Fauzan H, Hermansa Hadi, Wisnu Setiawan Hafidz, Muhammad Nasir Hanif, Abdullah Hanif, Kharis Hudaiby Helmiyah, Siti Herman Herman Herman Herman Herman Herman Yuliansyah, Herman Hermansa Husnul Dzikri Ibdal Imam Mahfudl Nasrulloh Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Indra Gunawan Iqbal Busthomi Iqbal Busthomi Irjayana, Rizky Caesar Izzan Julda D.E Purwadi Putra Jaka Dernata Jefree Fahana Juliansyah, Fitrah Kencana, Nagala Wangsa Kgs Muhammad Rizky Alditra Utama Khoirul Anam Dahlan Kusuma, Ridho Surya Lisna Zahrotun Lukman Nurhakim Maftukhah, Ainin Mardhiatul Ihsaniah Maulana, Irvan Maya Anggriani Miladiah Miladiah Miladiah, Miladiah Mirza Sutrisno Moch. Nasheh Annafii Mochammad Imron Awalludin Muhajir Yunus Muhammad Abdul Aziz Muhammad Abdul Aziz Muhammad Aziz Muhammad Aziz Muhammad Fahmi Mubarok Nahdli Muhammad Fauzan Gustafi Muhammad Fauzan Gustafi Muhammad Ihya Aulia Elfatiha Muhammad Irwan Syahib Muhammad Jundullah Muhammad Kunta Biddinika Muhammad Noor Fadillah Muhammad Noor Fadillah Muhammad Nur Ardhiansyah Muhammad Nur Faiz Muhammad Ridwan Muntiari, Novita Ranti Murinto Murinto Murinto Mustafa Mustafa Muzakkir Pangri N, Musri Iskandar Nagala Wangsa kencana Nasrulloh, Imam Mahfudl Nugroho, Ismail Adji Ockhy Jey Fhiter Wassalam Panggah Widiandana Periyanto, Ari Peryanto, Ari Prasetyo Hari Prabowo prayudi, Andi Puguh Wahyu Prasetyo, Puguh Purwanto Purwanto Purwono Purwono P Purwono, Purwono Putra, Alfiansyah Imanda Putra, Fajar R. B Putra, Fijaya Dwi Bima Sakti Putri Silmina, Esi Raden Mohamad Herdian Bhakti Rahmat Ardila Dwi Yulianto Ramdhani, Rezki Resmi - Aini Rezki Ramdhani Rezki Rezki Ridho Surya Kusuma Rifqi Rahmatika Az-Zahra Saad, Saleh Khalifah Sabarudin Saputra Sahiruddin Sahiruddin Sakti, Fijaya Bima Dwi Saleh khalifa saad Sarjimin, Sarjimin Sarmin, Sarmini Septa, Frandika Siswanto, Anwar Sonny Abriantoro Wicaksono Sri Rahayu Astari Sri Rahayu Astari Sri Winiarti Subandi, Rio Subhan Subhan Sugandi, Andi Sunardi Sunardi Sunardi, Sunardi Suwanti Suwanti Tarisno Amijoyo Tella, Fitriyani Tole Sutikno Tri Lestari Tri Lestari Tri Lestari Utama, Kgs Muhammad Rizky Alditra Utama, Kiagus Muhammad Rizky Aditra Veithzal Rivai Zainal Wala, Jihan Wasito Sukarno Widya Cholid Wahyudin Widya Cholid Wahyudin Widya Cholid Wahyudin Wilda Rina Hasibuan Winoto, Sakti Wisnu Dwi Haryanto Yuminah yuminah yuminah, Yuminah Yuminah, Yuminah