Integrating Ontology and Machine Learning for Enhanced Decision Support in Thyroid Disease Prediction

In recent years, decision support systems have emerged as valuable tools in the field of medical diagnosis and prediction. With the increasing prevalence of thyroid diseases, accurate and timely prediction plays a crucial role in improving patient outcomes. However, the adoption of traditional black-box machine learning models for thyroid disease prediction is hindered by their lack of interpretability, making it challenging for clinicians to trust and understand the decision-making process. Our innovative approach combines machine learning, specifically decision trees, with ontology integration for interpretability. The ontology classifier provides insights into the model's decision-making process. The dataset used comprises 9172 observations, each represented by 31 features, accessible for download on Kaggle. According to the results The ontology model surpassed Decision tree model.