Classification approach for covid-19 ICU admission using random forest and xgboost

COVID-19 has had a deep and far-reaching influence on global health, economics, and society, focusing on the crucial necessity of collaboration, scientific developments, and resilient healthcare systems in managing and mitigating infectious disease concerns from blood or any sort of human organs. The problem that exists nowadays in classification towards diseases, cancers and any sort of illness require a better accuracy from machine learning. However, there are multiple methods are available in finding best machine learning for classification area that has hard field in determine the best machine learning. Machine learning has showed potential in detecting, classifying and categorising unknown various type of data and threats. In this research, the propose in using machine learning classifiers to identify ICU admission. The classifiers used are Random Forest and XGBoost, which are well-known and commonly used in machine learning. For evaluate the efficiency of the technique, we first gather and pre-process a dataset gathered. Dataset are gathered from Kaggle and will be used to train and test the selected classifiers. The performance of these classifiers on the dataset is then examined, and their capacity to identify and classify Covid-19 for ICU is evaluated. We evaluate the performance of Random Forest and XGBoost to have the better approaches for classifying Covid-19 for ICU admission. The result will show that the suggested classifiers efficiently identify and classifying Covid-19 ICU for admission, outperforming existing approaches. Using machine learning classifiers for Covid-19 ICU admission, these can improve bioinformatics by leveraging continuous learning, algorithmic optimisation, feature engineering and iterative development processes. In conclusion, this research is an important step towards making accurate predictions and classifications, enabling data integration, and propelling research forward.