Standard-based personalized healthcare delivery for kidney illness using deep learning

Abstract

Motivation and objective. Predictive analytics is an active area of research in healthcare. It aims to provide better services to the patient and helps the medical practitioners to know what particular treatment would be suitable for a patient based on their past data. Deep learning is an emerging branch of machine learning in which deep artificial neural networks are used to learn a specific pattern for mapping input to output. It has revolutionized predictive analytics by achieving far better accuracy than conventional learning models. This paper aims to analyze the effect of deep learning on a standardized electronic health records dataset by diagnosing kidney-related diseases. Approach. The current study uses a general modularized deep learning architecture called encoder-combiner-decoder (ECD), which offers a robust framework. The model’s performance is enhanced by the availability of variations and extensions to the basic ECD architecture, corresponding to respective input and output feature types. The openEHR benchmark dataset (ORBDA) is used to train the model. It is a real-world dataset that has been provided by the Brazilian Public Health System through the SUS (DATASUS) Database Department of Informatics. Results. In the current research, the model trained using deep learning on the part of this benchmark dataset can help in diagnosing kidney-related illnesses. The evaluation metrics show high precision, recall, and F1 score for kidney-related diseases, meaning that they can be identified almost every time. Significance. The model is a novel attempt to analyze a standardized healthcare dataset that can be deployed in medical institutions in order for its performance to be evaluated by a medical professional.