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This study develops a pathological test recommendation system that leverages the Classifier Chain (CC) technique to enhance the accuracy and speed of test selection based on patient symptoms prior to physician consultation. By framing the recommendation task as a multi-label classification problem, the researchers achieved an impressive accuracy of 98.83% with the Logistic Regression model, while also employing Explainable AI (XAI) techniques to ensure clinical interpretability of the recommendations. The model's diagnostic reasoning aligns with established medical knowledge, thereby bolstering confidence in its application for timely and effective patient care.
Achieving 98.83% accuracy in test recommendations, this system not only accelerates diagnostic processes but also enhances clinical interpretability through Explainable AI.
Accurate and timely diagnoses are essential for quality patient care. However, delayed recommendation of diagnostic tests and physicians'subjective interpretations can hinder effective care. This study introduces a pathological test recommendation system that speeds up the test selection process using patient symptoms before physician consultation. The recommendation task is framed as a multi-label classification problem utilising the Classifier Chain (CC) technique to consider dependencies between tests. We collected data from the SOUTHERN.IML pathology and then created a custom dataset with the help of the expertise. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, were applied to compare models and identify the best fit for our study context. The Logistic Regression with CC model had the highest overall accuracy at 98.83%, while the Majority Voting ensemble model provided the best balance with a precision of 0.93, recall of 0.85, and F1-score of 0.89. To ensure transparency of the models and clinical interpretability, we used Explainable AI (XAI) techniques utilising SHAP (SHapley Additive Explanations), which identifies how each symptom is contributing to a test recommendation. The diagnostic reasoning revealed by the model was consistent with established medical knowledge of symptoms for the recommended tests, which further adds confidence to the model's reliability for diagnostic purposes. The reasoning could help physicians make logical decisions in critical scenarios. Overall, our findings suggest that CC can improve the efficiency of the traditional algorithms in diagnostic process providing accurate test recommendations.
