Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

Eduardo Enrique Contreras-Luján,Enrique Efrén García-Guerrero,Didier López-Mancilla,Everardo Inzunza-González,Esteban Tlelo-Cuautle,Oscar Roberto López-Bonilla

doi:10.3390/mca27020024

Abstract

Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. 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). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.

Highlights

Deep venous thrombosis (DVT) is a disorder in which blood clots form within the veins, obstructing the flow of blood through the circulatory system, and it affects people of all ages [1]
A two-class classification confusion matrix is developed to track the progress of the machine learning (ML) model trained, allowing the metrics to be validated and the process to be more dependable within the rubric by separating it into negative and positive DVT classifications, respectively
Each of them is kept with the true diagnosis and the diagnosis predicted by the ML algorithm; the first is True Negative (TN), in which both the true diagnosis and the ML prediction are negative, and the second is False Negative (FN), in which the ML diagnosis is negative but the true diagnosis is positive for DVT

Summary

Introduction

Deep venous thrombosis (DVT) is a disorder in which blood clots form within the veins, obstructing the flow of blood through the circulatory system, and it affects people of all ages [1]. The cause of the disease is unknown; it is thought to be caused by a combination of variables, including genetic factors. Genetic factors are thought to have a role in the diagnosis of the disorder. DVT is a disease that must be recognized as soon as possible because the implications might be fatal for the patient. Several scientists have created various techniques and methods to diagnose the problem over the years, beginning in the 1970s with the development of ultrasonography [4], which marked a breakthrough in the timely diagnosis of clots in the lower limbs of the human body. Philip Wells, a renowned scientist, has stated on numerous occasions that technology, which has been revolutionized exponentially in recent years, will support the future in the early diagnosis of diseases. This, combined with new trends in the work of computer equipment, will enable great advances in science and human health

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