This study proposes a novel approach that applies approximate inverse model explanations (AIME) on a stroke dataset to evaluate the factors that precipitate or prevent stroke occurrence. AIME helps explain the behavior of complex or less transparent AI and machine learning models (black-box models) and the basis of data instance estimates by constructing approximate inverse operators. Unlike previous methods, AIME yields highly interpretable explanations, thereby enhancing the transparency of complex AI and machine learning models for medical diagnosis. By employing AIME to construct an approximate inverse operator from a machine learning black-box model trained on a stroke dataset, this study aims to elucidate factors that may contribute to or mitigate the risk of stroke. Addressing this critical research gap, this method helps elucidate opaque black-box model decisions, potentially revolutionizing stroke risk assessment and prevention strategies. Future studies will aim to extend these interpretations into broader medical applications, foster discoveries, and improve patient outcomes through explainable AI. The potential scalability and adaptability of the proposed method suggest a promising future for medical AI, heralding a new era of explainable and dependable machine learning in healthcare.