Mortality Rate Analysis Covid-19 Patients Based on Condition Comorbidities with Approach K-Means Clustering

Abstract

The COVID-19 pandemic, which emerged in China in late 2019, rapidly spread to over 200 countries, including Indonesia. In response, various preventive measures were implemented to mitigate the increasing number of infections. West Sumatra Province ranked 11th out of 34 provinces in terms of confirmed COVID-19 cases. Patients infected with COVID-19 in this region often presented with comorbidities such as hypertension, diabetes mellitus, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), liver disorders, obesity, renal disease, and malignancies conditions known to contribute significantly to COVID-19-related mortality. This study aims to identify the mortality risk associated with comorbidities in COVID-19 patients using clustering analysis. A total of 91 patient records from a hospital in Padang City were analyzed. The data included age, primary and secondary diagnoses, all classified using the International Classification of Diseases (ICD-10). The K-Means Clustering algorithm was employed to categorize comorbidities into high, medium, and low-risk groups. Data processing was conducted using RapidMiner software, and accuracy was evaluated using mathematical calculations. The results indicated that Cluster 1 (high-risk) consisted of 5 comorbid diseases, Cluster 2 (medium-risk) included 9 diseases, and Cluster 3 (low-risk) encompassed 35 comorbid diseases. These findings demonstrate that clustering analysis is effective in classifying mortality risk based on comorbid conditions and can be utilized to support clinical prioritization and resource allocation during pandemic management.