Decision Tree Prediction Model in Patient Mortality Rate based on Risk Factors

Sri Handayani(1), Ririn Nurmandhani(2), Edi Jaya Kusuma(3), Sadono Wiwoho(4),

(1) Universitas Dian Nuswantoro
(2) Universitas Dian Nuswantoro
(3) Universitas Dian Nuswantoro
(4) Dinas Kesehatan Provinsi Jawa Tengah


The Coronavirus disease (Covid-19) has become a global problem since WHO declared a pandemic in 2020. The number of deaths due to Covid-19 has increased significantly in many countries. This study aimed to implement decision tree modeling to represent the relationship between risk factors and the mortality rate of Covid-19 patients. This study analyzed secondary data of 83,024 Covid patients from January 2020 to June 2021. Data processing used data mining with the decision tree classification method. The results showed that comorbidity is the leading risk factor for death which is then influenced by age. The higher the age group with comorbidities, the higher the risk of death. Suggested that health services can utilize the results of this study to prevent the severity of Covid-19 infection. Such as the development of comorbid awareness programs and community-based education on managing patients with comorbidities.


Covid-19; comorbid; mortality; decision trees; data mining

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