An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

María Berenice Fong-Mata,Oscar Roberto López-Bonilla,Didier López-Mancilla,Enrique Efrén García-Guerrero,David Abdel Mejía-Medina,Francisco Zamora-Arellano,Everardo Inzunza-González,Luis Jesús Villarreal-Gómez

doi:10.3390/electronics9111810

María Berenice Fong-Mata, Oscar Roberto López-Bonilla + Show 6 more

Open Access

https://doi.org/10.3390/electronics9111810

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Journal: Electronics	Publication Date: Nov 2, 2020
Citations: 8	License type: CC BY 4.0

Affiliation: Universidad de Tijuana, University of Guadalajara

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%.

Highlights

Venous thromboembolism (VTE), which includes deep-vein thrombosis (DVT) and pulmonary embolism (PE), in countries like the United States of America (USA) may affect up to 900,000 patients per year, with more than 300,000 deaths per year [1]
This paper proposes using a data augmentation technique [61,62,63,64] based on statistical data of real cases reported in [60] and from a public hospital, the above, with the purpose of making up a new dataset of synthetic cases represented by a matrix with 10,000 cases to be used in the training and validation of the proposed artificial neural network (ANN) model
ANNs, we can affirm that the above is similar to clinical cases in which the risk factors representing the input predictors to the system are equal in their entirety, obtaining a different resulting diagnosis as output from the CDSS

Summary

Introduction

Venous thromboembolism (VTE), which includes deep-vein thrombosis (DVT) and pulmonary embolism (PE), in countries like the United States of America (USA) may affect up to 900,000 patients per year, with more than 300,000 deaths per year [1]. DVT is a vascular condition in which a venous thrombus breaks off and travels through the bloodstream and, if it reaches the lungs, it might cause a fatal pulmonary embolism (PE) [2,3,4]. 100,000 inhabitants per year in USA and rises exponentially from

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