TY - GEN
T1 - Metabolomics in the prediction of prodromal stages of carotid artery disease using a hybrid ML algorithm
AU - Pezoulas, Vasileios
AU - Mishra, Pashupati P.
AU - Raitakari, Olli T.
AU - Kahonen, Mika
AU - Lehtimaki, Terho
AU - Fotiadis, Dimitrios I.
AU - Sakellarios, Antonis I.
N1 - Funding Information:
This project has received funding from the European Union's Horizon 2020 research and innovation programme TO-AITION under grant agreement No 848146.
Funding Information:
* This project has received funding from the European Union’s Horizon 2020 research and innovation programme TO_AITION under grant agreement No 848146.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Carotid artery disease (CAD) may be responsible for a stroke with fatal consequences for the patients. Early and non-invasive diagnosis and prediction of significantly high carotid intima media thickness (IMT) can reduce the death rates caused by cardiovascular disease. Machine learning can be applied for the development of robust models for this purpose when adequate data are available. In this work, we utilized metabolomics data from 2,147 patients in the Young Finns Study clinical trial to predict the high intima media thickness as a prodromal stage of the atherosclerotic carotid disease. An explainable AI based pipeline was developed which includes a novel employment of the Gradient Boosted Trees (GBT). More specifically, a hybrid loss function was used to adjust the effect of the dropout rates in the 'dart' booster in the loss function topology. The results of our analysis demonstrate that the novel implementation of the GBT improves the results in terms of the sensitivity which is the most important requirement to our analysis (accuracy 0.80, sensitivity 0.86, AUC 0.85). Moreover, it is shown that metabolomics can be used to increase sensitivity in predicting the increased IMT.
AB - Carotid artery disease (CAD) may be responsible for a stroke with fatal consequences for the patients. Early and non-invasive diagnosis and prediction of significantly high carotid intima media thickness (IMT) can reduce the death rates caused by cardiovascular disease. Machine learning can be applied for the development of robust models for this purpose when adequate data are available. In this work, we utilized metabolomics data from 2,147 patients in the Young Finns Study clinical trial to predict the high intima media thickness as a prodromal stage of the atherosclerotic carotid disease. An explainable AI based pipeline was developed which includes a novel employment of the Gradient Boosted Trees (GBT). More specifically, a hybrid loss function was used to adjust the effect of the dropout rates in the 'dart' booster in the loss function topology. The results of our analysis demonstrate that the novel implementation of the GBT improves the results in terms of the sensitivity which is the most important requirement to our analysis (accuracy 0.80, sensitivity 0.86, AUC 0.85). Moreover, it is shown that metabolomics can be used to increase sensitivity in predicting the increased IMT.
KW - explainable AI
KW - Machine learning
KW - metabolomics
KW - myocardial infarction
U2 - 10.1109/BHI56158.2022.9926774
DO - 10.1109/BHI56158.2022.9926774
M3 - Conference contribution
AN - SCOPUS:85143064542
T3 - IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
SP - 1
EP - 4
BT - 022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
PB - IEEE
T2 - IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Y2 - 27 September 2022 through 30 September 2022
ER -
