Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.

由于严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2)，2019 年冠状病毒病 (COVID-19) 于 2019 年 12 月底在全球范围内传播。缺乏早期诊断和成功的 COVID-19 评估，临床护理无效，死亡率高。在本研究中，我们使用机器学习方法研究生化指标的水平是否有助于区分和分类 COVID-19 的严重程度。该研究从生化指标的角度为COVID-19的诊断创造了一种高效的智能方法。该框架是通过将称为菌落捕食算法 (CPA) 的增强型新随机算法与内核极限学习机 (KELM)（缩写为 ECPA-KELM）相集成而提出的。该方法的核心特征是ECPA算法，它结合了灰狼优化器和飞蛾火焰优化器已经弃权的两个主要算子，以改进和恢复CPA研究功能，同时用于优化参数和选择特征对于凯尔姆。使用 IEEE CEC2017 基准对 ECPA 输出进行了彻底检查，以验证所提出方法的能力。最后，在使用生化指标诊断 COVID-19 时，使用了设计的 ECPA-KELM 模型和基于其他优化的其他竞争 KELM 模型。检查统计结果将显示所有指标的改进预测属性和更高的稳定性。