Developing a Predictive Model Predicting the Missing Appointment Using Deep Learning Algorithms

Abstract

In recent years, the use of predictive modelling techniques has gained significant attention in healthcare systems, aiming to improve operational efficiency and optimize resource allocation. One crucial area where predictive modelling can be applied is in predicting missing appointments, as missed appointments can lead to increased costs and inefficiencies in healthcare settings. This study focuses on developing a predictive model that uses deep learning algorithms to forecast the likelihood of a patient missing their scheduled appointment. Deep learning, a subset of machine learning, has shown promising results in various domains due to its ability to automatically extract relevant features from complex data. By using deep learning techniques, we aim to enhance the accuracy and reliability of predicting missed appointments. The predictive model is trained on a dataset containing patient demographics, historical appointment records, and other relevant features. Through the use of deep learning architectures such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), the model learns intricate temporal dependencies and subtle patterns in the data, enabling the correct prediction of missed appointments. The study contributes to the advancement of predictive analytics in healthcare by proving the potential of deep learning algorithms in improving appointment scheduling efficiency and resource allocation.