Deep Learning Approach to Predict Hemorrhage in Moyamoya Disease

02/01/2023

∙

Objective: Reliable tools to predict moyamoya disease (MMD) patients at risk for hemorrhage could have significant value. The aim of this paper is to develop three machine learning classification algorithms to predict hemorrhage in moyamoya disease. Methods: Clinical data of consecutive MMD patients who were admitted to our hospital between 2009 and 2015 were reviewed. Demographics, clinical, radiographic data were analyzed to develop artificial neural network (ANN), support vector machine (SVM), and random forest models. Results: We extracted 33 parameters, including 11 demographic and 22 radiographic features as input for model development. Of all compared classification results, ANN achieved the highest overall accuracy of 75.7 CI, 68.6 forest with 70.0 framework can be a potential effective tool to predict the possibility of hemorrhage among adult MMD patients based on clinical information and radiographic features.

READ FULL TEXT

Deep Learning Approach to Predict Hemorrhage in Moyamoya Disease

AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning

A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder

Two Tier Prediction of Stroke Using Artificial Neural Networks and Support Vector Machines

Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

Machine Learning Approach to Earthquake Rupture Dynamics

Deep Learning Approach to Predict Hemorrhage in Moyamoya Disease

Related Research

AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning

A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder

Two Tier Prediction of Stroke Using Artificial Neural Networks and Support Vector Machines

Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

Machine Learning Approach to Earthquake Rupture Dynamics