Background: Deep vein thrombosis (DVT) is a common multifactorial disease. If the disease is not prevented as soon as possible, it may prolong the cure time and even lead to death.
Aims: To develop a simple, cost-effective, accurate and non-invasive prediction model by artificial intelligence machine-learning model for early detection of high-risk DVT patients.
Methods: 1295 patients were selected including all 729 patients with
DVT and 566 patients without from January 2011 to December 2020 in the 6th
Medical Centre of Chinese PLA General Hospital. An artificial neural network (ANN) predictive procedure divided three-quarters of the patients (993 cases) randomly as training sample data to construct the predictive model while one quarter of the
patients (302 cases) used to test the effectiveness of the constructed model. ANN inputs were gender, age, fibrinogen, D-Dimer, hypertension, coronary heart disease, tumor,diabetes, chemotherapy, surgery etc.
Results: When the number of hidden-nodes was set to 8 and the number of epochs was set to 800 in the artificial neural network model, the accuracy reached the
highest with an average rate of 81.47% and variance of 0.0001. The prediction
accuracy of ANN model was higher than that of linear Logistic regression model
with an average rate of 79.80%.
Conclusion(s): With the help of computer-based ANN model, the high-risk DVT
patients could be detected as early as possible, conducive to disease prevention and the DVT incidence reduction.
To cite this abstract in AMA style:Yang Y, Zhongbin Z, Guo Y. Predicting DVT Incidence based on risk factors by using Artificial Neural Networks [abstract]. https://abstracts.isth.org/abstract/predicting-dvt-incidence-based-on-risk-factors-by-using-artificial-neural-networks/. Accessed October 1, 2023.
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ISTH Congress Abstracts - https://abstracts.isth.org/abstract/predicting-dvt-incidence-based-on-risk-factors-by-using-artificial-neural-networks/