An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria
Maria Berenice Fong-Mata,E. E. García-Guerrero,David Abdel Mejía-Medina,Oscar Roberto López-Bonilla,Luis Jesús Villarreal-Gómez,Francisco Zamora-Arellano,D. López-Mancilla,Everardo Inzunza-González +7 more
Reads0
Chats0
TLDR
A new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset and was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases.Abstract:
The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.read more
Citations
More filters
Journal ArticleDOI
Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning.
Bernhard Kainz,Mattias P. Heinrich,Antonios Makropoulos,Jonas Oppenheimer,Ramin Mandegaran,Shrinivasan Sankar,Christopher Deane,Sven Mischkewitz,Fouad Al-Noor,Andrew Rawdin,Andreas Ruttloff,Mark Stevenson,Peter Klein-Weigel,Nicola Curry +13 more
TL;DR: In this paper, a deep learning approach for the automatic interpretation of compression ultrasound images is proposed to detect DVT. But, the method is not suitable for non-specialists.
Journal ArticleDOI
Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning
TL;DR: In this paper, the authors extended the power level measurement by using multiple anchors and multiple radio channels and, consequently, considered different approaches to aligning the actual measurements with the recorded values.
Journal ArticleDOI
Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study
José Jaime Esqueda-Elizondo,Reyes Juárez-Ramírez,Oscar Roberto López-Bonilla,E. E. García-Guerrero,Gilberto Galindo-Aldana,Laura Jiménez-Beristáin,Alejandra Serrano-Trujillo,Esteban Tlelo Cuautle,Everardo Inzunza-González +8 more
TL;DR: A methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD is presented and it makes it possible to develop better learning scenarios according to the person’s needs with ASD.
Journal ArticleDOI
Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis
Eduardo Enrique Contreras-Luján,E. E. García-Guerrero,Oscar Roberto López-Bonilla,Esteban Tlelo-Cuautle,D. López-Mancilla,Everardo Inzunza-González +5 more
TL;DR: This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM) and the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis.
Journal ArticleDOI
Evaluation of Machine Learning Algorithms for Classification of EEG Signals
Francisco Javier Ramirez-Arias,E. E. García-Guerrero,Esteban Tlelo-Cuautle,Juan Miguel Colores-Vargas,Eloísa García-Canseco,Oscar Roberto López-Bonilla,Gilberto Galindo-Aldana,Everardo Inzunza-González +7 more
TL;DR: This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements and suggests the applicability of this approach to different scenarios, such as implementing robotic prostheses.
References
More filters
Journal ArticleDOI
Artificial neural networks: a tutorial
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
Journal ArticleDOI
Evolving artificial neural networks
TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
Journal ArticleDOI
The Epidemiology of Venous Thromboembolism
TL;DR: Early mortality after VTE is strongly associated with presentation as PE, advanced age, cancer, and underlying cardiovascular disease, with a significantly higher incidence among Caucasians and African Americans than among Hispanic persons and Asian‐ Pacific Islanders.
Journal ArticleDOI
Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes
TL;DR: An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
Journal ArticleDOI
The epidemiology of venous thromboembolism
TL;DR: Venous thromboembolism is a complex disease, involving interactions between acquired or inherited predispositions to thrombosis and VTE risk factors, including increasing patient age and obesity, hospitalization for surgery or acute illness, nursing-home confinement, active cancer, trauma or fracture, immobility or leg paresis, superficial vein thromBosis, and, in women, pregnancy and puerperium.
Related Papers (5)
Machine Learning Based Methods for Handling Imbalanced Data in Hepatitis Diagnosis
Azam Orooji,Farzaneh Kermani +1 more
Improvement the Accuracy of Six Applied Classification Algorithms through Integrated Supervised and Unsupervised Learning Approach
Ahvaz,Manchester +1 more