Explainability Based on Feature Importance for Better Comprehension of Machine Learning in Healthcare

The use of Artificial Intelligence (AI) in healthcare is getting more prevalent, encompassing responsibilities like intelligent medical diagnoses and operative robots. The accuracy and performance of AI systems are prioritized by Machine Learning (ML) engineers while medical professionals are more interested in their applicability and usefulness in clinical settings. Unfortunately, medical practitioners often lack the necessary skills to interpret AI-based systems, limiting the usage of the tools that enhance healthcare solutions, automating routine analysis tasks and limiting expertise available for validation. Explainable Artificial Intelligence(XAI) is a field that focuses on methods to help understand and interpret ML models. However, most XAI research has been from a viewpoint of Computer Science (CS), with little focus on supporting other domains like healthcare. In this work, a straightforward solution is presented to increase the explainability of ML models to professionals from non-CS domains like healthcare experts. The suggested method integrates feature importance that assesses the influence of distinct features on AI-based system outcomes into standard ML workflows. This could permit medical experts to better understand AI-based systems, improving their ability to comprehend the usefulness and applicability of ML models.