ANALISIS DAN PERBANDINGAN KELOMPOK PASIEN COVID-19BERDASARKAN KOMORBIDITAS MENGGUNAKAN K-MEANS CLUSTERING - Hilma Fitri Solehah (2021)
The COVID-19 pandemic that has occurred since the beginning of 2020 is a new problem and challenge that is felt by all around the world. The transmission of the SARS-CoV-2 virus infection is very easy and fast to spread among us. Comorbidity is one of the risk factors that is susceptible to the virus infection. To identify any comorbid groups that can be found in COVID-19 patients, an analysis study using data mining method with K-Means Clustering algorithm will help to do grouping by the clustering results from the patients based on the proportion of comorbidities and age ranges that found in several populations. The data to be processed is obtained from a valid source Covid Analytics which contains a summary of medical research data for COVID-19 patients from various countries. The cluster results are 3 clusters with different clinical degrees from mild to severe. There are 321 data (39%) of the COVID-19 patients group who were treated in the first cluster with an average age of 48 years old and the most found comorbidity was diabetes. The second cluster consisted of 370 data (44%) with an average age of 64 years old with diabetes as the most found comorbidity. Meanwhile, the third cluster consisted of 140 data (17%) with an average age of 68 years and the highest comorbidity was hypertension. This analysis using K-Means Clustering can provide an overview of different groups of patients also with different clinical degrees and different forms of treatment.