Analysing and Identifying COVID-19 Risk Factors using Machine Learning Algorithm with Smartphone Application

The priority of this research is to diagnose mortality risk factors faster using a lightweight model (LightGBM). We will include potential information to determine the ground-breaking problem and the research gap. In our study, C-reactive protein (CRP) is taken to measure the impact on Covid-19 as it seems to increase CRP levels significantly due to inflammatory reactions and related tissue destruction (Sproston and Ashworth 2018). Higher concentrations indicate more severe disease - linked to lung damage and worse prognosis. However, our literature review observed that it was not considered in most previous studies to identify mortality risk factors. Therefore, we have decided to include it, and our utmost desire was to build a system with the most accurate and progressive result. In this research, our first novel contribution is to develop an ML technique utilising the medical variables for identifying COVID-19 mortality risk to provide quick and promising accuracy. Secondly, an Android application will be integrated with the system, which will serve as a benchmark for clinical applications. Finally, in addition to this, it will create awareness that will significantly contribute to the society around the globe. The proposed system will be aligned with our developed system, World health Aid (WHA), a telemedicine platform and TikarBoi, a vaccination diary. Both of these applications are interlinked and are available in the App Store and Google Play. They provide expert care to users who are denied the wellbeing they need through geographical, disability or other limiting factors. Read Less