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

Abstract

Motivation and Objective: Predictive analytics is one of the active
areas of research in healthcare. It aims to provide better services to
the patient and helps the medical practitioners to know what particular
treatment a patient may need 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 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) dataset 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 kidneyrelated
illnesses.The evaluation metrics show high precision, recall, and
F1 score for kidney-related disease, representing that they can be
identified almost every time.
Significance: The model is a novel attempt on analyzing a standardized
healthcare dataset that can be deployed in medical institutions to
evaluate its performance by a medical professional.