Bleak Medical Prognosis of Covid Patients Using Explainable Machine Learning

Today, artificial intelligence (AI) algorithms are used to generate predictions in many high-stakes fields, such as credit risk analysis and medical diagnosis. Therefore, AI systems are having a greater impact on people, yet many cutting-edge technologies are opaque, negating people's "right to explanation." To address this issue, experts in the field have created explainable AI to detail the rationale behind an algorithm's prediction. The focus of this article is on creating an explanation system for a predicted outcome and a machine learning prediction model for poor medical prognosis in Covid patients. Many different types of machine learning algorithms, including both foundational and augmented versions, have been implemented. It was shown that the XGboost model provided superior diagnostic performance. This situation of a prediction has been explained using Shapley Additive Explanations (SHAP). The reason the XGBoost machine learning model gives a certain prediction result may be deduced with the use of three statistics charts.