## 7 Conclusions  
This study introduced machine learning models for predicting the severity of illness in patients diagnosed with COVID-19. The prediction is made based on a patient's medical history and symptoms on arrival at the hospital. The MLP machine learning models achieved 78-80% prediction accuracy while the models produced 65-80% prediction accuracy. Our machine learning models were combined with the LIME explainability model and then integrated into a mobile application, which can support medical staff in performing effective triage based on medical tests to reduce a patient's risk of medical deterioration.  
Figure 5: COVID-19 application user-interface (UI): (**a**) home page, (**b**) patient details form, (**c**,**d**) prediction of a patient’s severity of illness for models A and B, respectively, and (**e**) an explanation of the prediction.  
In addition, our explainability model can offer further insight regarding an individual patient's medical condition and help identify the factors critically affecting the patient's illness severity. The machine learning models and the mobile application are provided as an open-source code, making them accessible and allowing for further development and extension. The explainable model and mobile application source code is available through the GitHub repository: [[https://github.com/freddygabbay/covid19](https://github.com/freddygabbay/covid19) explainableML]([https://github.com/freddygabbay/covid19](https://github.com/freddygabbay/covid19) explainableML), (accessed on 28 October 2021)  
We highly encourage future work to extend and enhance this study in the following ways:  
1. An exploration using another high-quality dataset is suggested to further understand the models' performance and limitations.
2. We also encourage a further examination of transfer-learning methods, which can adapt the learning process from one dataset to other datasets.
3. Due to the structure of the database, the model is unable to predict deterioration or amelioration (i.e., a transition from one level of severity to another) based on demographic variables, medical history, and type of intervention. This is beyond the scope of this paper but should be attempted in future studies.
4. Future studies are also encouraged to develop models that can generate a timeline depicting the progression of illness.  
This research uses an open dataset provided by the Mexican Federal Health Secretary through the General Director of Epidemiology: [[https://www.gob.mx/salud/documents/datos-abieros-152127](https://www.gob.mx/salud/documents/datos-abieros-152127)]([https://www.gob.mx/salud/documents/datos-abieros-152127](https://www.gob.mx/salud/documents/datos-abieros-152127)). Accessed on 11 October 2021.  
Conceptualization, F.G. and S.B-L; methodology, F.G.; software, N.H., O.M. and F.G; validation, N.H. and O.M.; formal analysis, N.H., O.M., S.B.-L. and F.G.; investigation, N.H., O.M., S.B.-L. and F.G.; resources, F.G. and S.B.-L.; data curation, F.G. and S.B.-L.; writing--original draft preparation, F.G. and S.B.-L.; writing--review and editing, F.G. and S.B.-L.; visualization, N.H., O.M., S.B.-L. and F.G.; supervision, F.G. and S.B.-L.; project administration, F.G.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study is based on an anonymized, publicly available database. The study was conducted according to the guidelines of the Ruppin Academic Center Research.  
Patient consent was waived since the used dataset was anonymized and publicly available on behalf of the Mexican Federal Health Secretary, General Director of Epidemiology.  
The authors declare no conflict of interest.