Explainable Artificial Intelligence-Based Disease Prediction with Symptoms Using Machine Learning Models

Artificial intelligence (AI) has the potential to revolutionize the field of healthcare by automating many tasks, enabling more efficient diagnosis and treatment. However, one of the challenges with AI in healthcare is the need for explainability, as the decisions made by these systems can have serious consequences for patients. AI can be particularly useful in the classification of diseases based on symptoms. This involves using machine learning algorithms to analyze a patient’s symptoms and classify them as having a particular disease or condition. While using black box machine learning algorithms can be highly accurate, there is little to no understanding on how these models work. Therefore, using techniques such as feature importance analysis and Explainable AI, it is possible to provide clear explanations for the decision-making process, which can improve trust and understanding among healthcare providers and patients.