Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data

被引：1

作者：

Cugliari, Giovanni ^{[1
]}

Benevenuta, Silvia ^{[1
]}

Guarrera, Simonetta ^{[1
]}

Sacerdote, Carlotta ^{[2
]}

Panico, Salvatore ^{[3
]}

Krogh, Vittorio ^{[4
]}

Tumino, Rosario ^{[5
]}

Vineis, Paolo ^{[6
]}

Fariselli, Piero ^{[1
]}

Matullo, Giuseppe ^{[1
]}

机构：

[1] Univ Turin, Dept Med Sci, Turin, Italy

[2] Piedmont Reference Ctr Epidemiol & Canc Prevent, Turin, Italy

[3] Federico II Univ Naples, Dept Clin Med & Surg, Naples, Italy

[4] IRCCS Ist Nazl Tumori, Epidemiol & Prevent Unit, Milan, Italy

[5] Dept Canc Registry & Histopathol, Ragusa, Italy

[6] Sch Publ Hlth, Dept Epidemiol & Biostat, London, England

来源：

2019 16TH IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY - CIBCB 2019 | 2019年

关键词：

Epigenetic biomarkers; DNA methylation; Genomics; Computational statistics;

D O I：

10.1109/cibcb.2019.8791483

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Classically, the cardiovascular risk of individual is evaluated using phenomenological variables (PV) such as blood pressure, body mass, smoker status, gender, age etc. Here we show that, on prospective study (after 10-15 years) these PV display a poor agreement with case-control samples. We were able to obtain more accurate predictions using both DNA methylation data and PV as input features of a Random Forest model, achieving a ROC-AUC of 0.74. Furthermore, the Random Forest output correlates with the reliability of the predictions producing a ROC-AUC of 0.90 when only the most reliable predictions are taken into consideration.

引用

页码：39 / 42

页数：4

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