Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/27/2/24 |
| _version_ | 1797434442367631360 |
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| author | Eduardo Enrique Contreras-Luján Enrique Efrén García-Guerrero Oscar Roberto López-Bonilla Esteban Tlelo-Cuautle Didier López-Mancilla Everardo Inzunza-González |
| author_facet | Eduardo Enrique Contreras-Luján Enrique Efrén García-Guerrero Oscar Roberto López-Bonilla Esteban Tlelo-Cuautle Didier López-Mancilla Everardo Inzunza-González |
| author_sort | Eduardo Enrique Contreras-Luján |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:32:18Z |
| format | Article |
| id | doaj.art-a9421b84fd23442a9fe4bfe3cc779ac0 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-09T10:32:18Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-a9421b84fd23442a9fe4bfe3cc779ac02023-12-01T21:12:27ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472022-03-012722410.3390/mca27020024Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous ThrombosisEduardo Enrique Contreras-Luján0Enrique Efrén García-Guerrero1Oscar Roberto López-Bonilla2Esteban Tlelo-Cuautle3Didier López-Mancilla4Everardo Inzunza-González5Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada C.P. 22860, Baja California, MexicoFacultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada C.P. 22860, Baja California, MexicoFacultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada C.P. 22860, Baja California, MexicoDepartamento de Electrónica, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, Santa María Tonanzintla, San Andrés Cholula, Puebla C.P. 72840, San Andrés Cholula, MexicoCentro Universitario de Los Lagos, Universidad de Guadalajara, Enrique Díaz de León, Col. Paseos de la Montaña, Lagos de Moreno C.P. 47460, Jalisco, MexicoFacultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada C.P. 22860, Baja California, MexicoDeep 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.https://www.mdpi.com/2297-8747/27/2/24DVTearly diagnosisartificial intelligencemachine-learningsmart systemembedded system |
| spellingShingle | Eduardo Enrique Contreras-Luján Enrique Efrén García-Guerrero Oscar Roberto López-Bonilla Esteban Tlelo-Cuautle Didier López-Mancilla Everardo Inzunza-González Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis Mathematical and Computational Applications DVT early diagnosis artificial intelligence machine-learning smart system embedded system |
| title | Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis |
| title_full | Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis |
| title_fullStr | Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis |
| title_full_unstemmed | Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis |
| title_short | Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis |
| title_sort | evaluation of machine learning algorithms for early diagnosis of deep venous thrombosis |
| topic | DVT early diagnosis artificial intelligence machine-learning smart system embedded system |
| url | https://www.mdpi.com/2297-8747/27/2/24 |
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