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Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction
Hua Ye1, Peiliang Wu2, Tianru Zhu3
1Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical University Yueqing 325600 China.
Insights
A new intelligent model, Harris Hawks Optimization-Fuzzy K-Nearest Neighbor (HHO-FKNN), accurately predicts COVID-19 severity. This AI tool aids clinical decisions by identifying key indicators for severe cases.
Area of Science:
- Artificial Intelligence
- Medical Informatics
- Computational Biology
Background:
- Accurate COVID-19 severity prediction is crucial for clinical decision-making.
- Existing models may lack the precision needed for nuanced patient stratification.
- Identifying key predictive factors remains an ongoing challenge.
Purpose of the Study:
- To develop an intelligent prediction model for distinguishing COVID-19 severity.
- To provide a reliable tool for assisting clinical diagnostic decisions.
- To enhance the accuracy and stability of COVID-19 severity classification.
Main Methods:
- Proposing the Harris Hawks Optimization-Fuzzy K-Nearest Neighbor (HHO-FKNN) model.
- Utilizing patient data including demographics, comorbidities, symptoms, and immune markers.
- Optimizing Fuzzy K-Nearest Neighbor (FKNN) parameters and feature subsets using Harris Hawks Optimization (HHO).
Main Results:
- The HHO-FKNN model demonstrated superior classification performance and stability compared to other machine learning algorithms.
- The model effectively identified critical features differentiating severe from mild COVID-19 cases.
- Comparative experiments validated the enhanced accuracy and reliability of the HHO-FKNN approach.
Conclusions:
- The HHO-FKNN model is a promising tool for predicting COVID-19 severity.
- This approach offers a more objective reference for clinical diagnostic decisions.
- The model's ability to identify key features contributes to a better understanding of severe COVID-19.
Abstract:
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
