Prediction Models of Ischemic Stroke Using Deep Neural Networks
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
The ischemic stroke is one of the leading causes of death worldwide. Although,
there are many known risk factors for the disease the growing amount of electronic
medical data available gives opportunities for creating novel models for personal risk
prediction. Usage of deep neural network (DNN) for developing such models can offers
many benefits such as potential to encode multiple types of data, less feature selection and
engineering required, and sometimes even an increased prediction accuracy. This Thesis
focuses on developing a model for ischemic stroke prediction using electronic health
record (EHR) data. I show that TabNet, a state-of-the art DNN architecture for tabular
data analysis outperforms a simpler method, the FastAI tabular learner. Still, neither
of the DNN methods achieved better results than the Random Forest. The ensemble
models using Random Forest and DNN models were tested but only a small increase in
the performance was achieved compared to the singular model. These results indicate
that an ensemble-based methods such as Random Forest is sufficient for the data used.
Nevertheless, with increased number of features and addition of more complex data types
methods such as TabNet could still become valuable. All models developed resulted with
high prediction power for ischemic stroke. This indicates that personal risk predictions
for ischemic stroke can be given and the clinical utility of the models should be evaluated
further.
Description
Keywords
machine learning, neural networks, ischemic stroke