Josephus Bernhard O, Nawir Ardianto H, Wijaya Evelyn, Moniaga Jurike V, Ohyver Margaretha
Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480.
Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480.
Procedia Comput Sci. 2021;179:871-877. doi: 10.1016/j.procs.2021.01.076. Epub 2021 Feb 19.
The spread of COVID-19 has made the world a mess. Up to this day, 5,235,452 cases confirmed worldwide with 338,612 death. One of the methods to predict mortality risk is machine learning algorithm using medical features, which means it takes time. Therefore, in this study, Logistic Regression is modeled by training 114 data and used to create a prediction over the patient's mortality using nonmedical features. The model can help hospitals and doctors to prioritize who has a high probability of death and triage patients especially when the hospital is overrun by patients. The model can accurately predict with more than 90% accuracy achieved. Further analysis found that age is the most important predictor in the patient's mortality rate. Using this model, the death rate caused by COVID-19 could be reduced.
新型冠状病毒肺炎（COVID-19）的传播让世界陷入混乱。截至目前，全球确诊病例达5235452例，死亡338612例。预测死亡风险的方法之一是使用医学特征的机器学习算法，这需要时间。因此，在本研究中，通过训练114个数据对逻辑回归进行建模，并使用非医学特征对患者的死亡率进行预测。该模型可以帮助医院和医生确定哪些患者死亡概率高，并对患者进行分类，尤其是在医院不堪重负时。该模型能够以超过90%的准确率进行准确预测。进一步分析发现，年龄是患者死亡率的最重要预测因素。使用该模型，可以降低COVID-19导致的死亡率。
