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

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.

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

“…The ML algorithms proposed in this research study could be implemented in highperformance embedded systems or edge computing devices as verified in previous studies [59,89]. These act as the central control system, which is in charge of communicating with the BCI to acquire EEG signals.…”

“…Recent studies have focused on the proper selection of EEG signal characteristics and its effect on the accuracy of ML and DL algorithms, as presented in [30]. ML and DL techniques are widely accepted and help to develop specific tasks within different applications [55][56][57][58][59][60][61]. Moreover, they are increasingly used to obtain EEG data for pattern analysis, classification of group membership, and BCIs [29,[62][63][64][65][66][67].…”

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

“…Additionally, Villacorta et al 90 used ML-based models to improve traditional CPMs (Geneva, 23 Wells, 22 and PERC 95 ) in the diagnosis of PE but further external validation is needed. Contreras-Luján et al 80 explored several ML algorithms on Wells criteria to predict DVT in a synthetic dataset with Support Vector Machines (SVM) algorithms as the best algorithms. Although their results are probably overoptimistic, their study is innovative in that they were able to deploy the diagnostic model on a Raspberry Pi4 (RPi4) device that can eventually be connected to sensors, wearables, and other sources to collect patient data in real-time and generate predictions.…”

“…Overall, 15 studies explored ML-based models to assist VTE diagnosis, either in the form of a pretest probability or to assist diagnosis after clinical presentation (Supplementary Table 4) [79][80][81][82][83][84][85][86][87][88][89][90][91][92][93] . Most of them do not describe clearly preprocessing steps, splitting/cross-validation, hyperparameters, and other performance metrics.…”

Evaluating Agile Neural Educational System for Effective Resource Management

“…The evaluation of a model is a necessity to know the level of efficiency and the degree of accuracy to enhance the validity and reliability of the model (Contreras-Luja´n et al, 2022). Many research works on model evaluation show a variety of methodologies in evaluating a model to justify its usefulness.…”