Penerapan Analisis Biplot Robust Singular Value Decomposition Untuk Data Penyakit Jantung Di Kabupaten Karo
DOI:
10.29303/jm.v8i1.11421Published:
2026-01-30Downloads
Abstract
Heart disease is one of the leading causes of death worldwide, including in Indonesia. WHO data from 2023 records 17.8 million deaths due to heart disease. Meanwhile, in the Karo region from June 2024 to August 2024, around 1,616 patients suffered from heart disease and received outpatient treatment. The main factors causing heart disease include gender, age, blood pressure, diabetes, cholesterol, family history, and smoking habits. This study aims to analyze risk factors associated with heart disease and visualize the relationship patterns between risk factor variables and heart disease using the RSVD Biplot method. This method detects 5 outliers in congenital heart disease data with a GOF of 51.24% and visualizes the relationships between variables in a two-dimensional space. The results of this study show that the primary factors significantly associated with heart disease are high cholesterol, where smoking leads to increased blood pressure. High blood pressure causes damage to blood vessel walls. Visualizing the relationship patterns between factors related to heart disease makes it easier to see how variables interconnect in low dimensions. Through visualization, we can identify which factors most influence heart conditions. Thus, the RSVD Biplot method provides insight into the complex relationships between risk factors and heart disease, enabling the design of more effective heart disease prevention strategies.
Keywords:
Heart Disease Biplot Robust Singular Value DcompositionReferences
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