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Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. The utilization of AI in healthcare, particularly in dental clinics, has drawn attention to the issue of appointment no-shows. These no-shows have detrimental effects such as increased waiting times, limited-service access, and financial burden on healthcare providers. Therefore, optimizing the organization of dental clinics is crucial to effectively cater to a diverse patient population with varying dental needs, especially considering the projected rise in demand for dental care. To address the problem of appointment no-shows, the researchers proposed a programming model that harnesses machine learning algorithms. Three specific algorithms, namely Decision Trees, Random Forest, and Multilayer Perceptron, were employed, with the Multilayer Perceptron being used for the first time in this particular context. The researchers collected a dataset from five dental facilities specializing in nine areas and employed Explainable AI techniques to gain insights into the factors contributing to patient absences. The model's performance was evaluated using multiple metrics. The Decision Tree model exhibited favorable accuracy, achieving 79% precision, 94% recall, 86% F1-Score, and 84% AUC (Area Under the Curve). The Random Forest model demonstrated even higher accuracy, with 81% precision, 93% recall, 87% F1-Score, and 83% AUC. Similarly, the Multilayer Perceptron model attained an accuracy of 80% precision, 91% recall, 86% F1-Score, and 83% AUC. |
