Implementation of K-Means Algorithm to Group Age of Cardiovascular Disease Patients

Cardiovascular disease, including coronary heart disease, peripheral arteries and heart failure, is a serious disease that is the leading cause of death globally. Risk factors such as high blood pressure, dyslipidemia, smoking, diabetes, and obesity contribute to the development of this disease. This study aims to group cardiovascular disease sufferers based on age using the k-means clustering method with optimization of the k value using the elbow method. The data used comes from more than 35,000 preprocessed observations. The analysis results show that the optimal number of clusters is five. Data preprocessing succeeded in cleaning the data from missing values, and the elbow method helped determine the number of clusters that were relevant for age grouping of cardiovascular disease sufferers. The results of this grouping can be used for further analysis in efforts to prevent and manage cardiovascular disease.

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Publication Details

Journal
Journal Of Data Insights
Published
2025-06-30
DOI
https://doi.org/10.26714/jodi.v3i1.216
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Implementation of K-Means Algorithm to Group Age of Cardiovascular Disease Patients

Prizka Rismawati Arum, Tiani Wahyu Utami, M. Rahmi
Journal Of Data Insights
Artificial Intelligence in Healthcare
article

Implementation of K-Means Algorithm to Group Age of Cardiovascular Disease Patients

Prizka Rismawati Arum, Tiani Wahyu Utami, M. Rahmi
article en

Abstract

Cardiovascular disease, including coronary heart disease, peripheral arteries and heart failure, is a serious disease that is the leading cause of death globally. Risk factors such as high blood pressure, dyslipidemia, smoking, diabetes, and obesity contribute to the development of this disease. This study aims to group cardiovascular disease sufferers based on age using the k-means clustering method with optimization of the k value using the elbow method. The data used comes from more than 35,000 preprocessed observations. The analysis results show that the optimal number of clusters is five. Data preprocessing succeeded in cleaning the data from missing values, and the elbow method helped determine the number of clusters that were relevant for age grouping of cardiovascular disease sufferers. The results of this grouping can be used for further analysis in efforts to prevent and manage cardiovascular disease.

Journal Of Data InsightsVol. 3(1)
Openalex Percentile: Top 82%
Artificial Intelligence in Healthcare
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