Abstract—COVID-19 (coronavirus disease) has spread worldwide and has become a pandemic, which causes by the SARS-CoV2 virus. Because the number of cases increases daily, interpreting the laboratory findings takes time, resulting in limitations of findings. Because of these limitations, the need for a clinical decision-making system with predictive algorithms has arisen. By identifying diseases, predictive algorithms would be able to reduce the strain on healthcare systems. In this work, we developed clinical predictive models using machine learning techniques with the help SMOTE+ENN Hybrid technique and laboratory data to develop models that can accurately predict which patients will receive COVID-19. To evaluate our prediction models in this work, precision, F1-score, recall AUC, and Accuracy evaluation metrics are employed. From 600 patients and 10 laboratory findings, the different models are tested and validated with 10-fold cross-validation and holdout cross-validation approaches. The experimental results show that our predictive models can correctly identify patients with COVID-19 with an accuracy of 98.28%, an F1-score of 98.27%, a precision of 98.23%, a recall of 98.32%, and an AUC of 98.32% in the holdout cross-validation approach, and an accuracy of 97.42%, and F1-score of 97.82%, a precision of 97.63%, a recall of 98.05%, and an AUC of 92.66% in 10-fold cross-validation approach. The results of the experiments showed that all machine learning models in the holdout cross-validation approach outperformed the 10-fold cross-validation approach. Finally, to help medical experts with accurately prioritizing resources, predictive models based on laboratory findings have been discovered that can assist in predicting COVID-19 infection and assisting medical professionals to identify which medical resources are most valuable.
 

Index Terms—artificial intelligence, SARS-CoV2, machine learning, COVID-19, SMOTE+ENN, Imbalanced dataset

Cite: Bilal Abdualgalil, Sajimon Abraham, and Waleed M. Ismael, "COVID-19 Infection Prediction Using Efficient Machine Learning Techniques Based on Clinical Data," Journal of Advances in Information Technology, Vol. 13, No. 5, pp. 530-538, October 2022.

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