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| Volume 11 Issue 1 Year 2016 | ||
The use of optimal partitionings for multiparameter data analysis in clinical trials
Guliev R.R., Senko O.V., Zateyshchikov D.A., Nosikov V.V., Uporov I.V., Kuznetsova A.V., Evdokimova M.A., Tereshchenko S.N., Akatova E.V., Glaser M.G., Galyavich A.S., Koziolova N.A., Yagoda A.V., Boeva O.I., Shlyk S.V., Levashov S.Y., Konstantinov V.O., Brazhnik V.A.,Varfolomeev S.D., Kurochkin I.N. Emanuel Institute of Biochemical Physics of Russian Academy of Science, Moscow, Russia
Central State Medical Academy of Department of Presidential Affairs, Moscow, Russia Computer Center of Russian Academy of Science, Moscow, Russia Lomonosov Moscow State University, Moscow, Russia City Clinical Hospital No. 51, Moscow, Russia Institute of Cardiology, Russian Cardiology Scientific and Production Center, Moscow, Russia Moscow State University of Medicine and Dentistry named after A.I. Evdokimov, Moscow, Russia Sechenov Moscow Medical Academy, Moscow, Russia Kazan State Medical University, Kazan, Russia Perm State Medical University named after E.A. Vagner, Perm, Russia Stavropol State Medical University, Stavropol, Russia Rostov State Medical University, Rostov-on-Don, Russia Chelyabinsk state medical academy, Chelyabinsk, Russia North-Western State Medical University named after Mechnikov, St. Petersburg, Russia Abstract. A predictive model is presented which allows estimating six-month-risk of cardiovascular disease in patients discharged from hospital after acute coronary syndrome. A database, that has been collected from 16 medical centers in seven Russian cities during seven years, was used to create the model. The database contains a wide range of clinical, biochemical and genetic characteristics. The approaches based on the use of optimal partitioning, such as the method of optimal valid partitioning (OVD) and the modified method of statistically weighted syndromes (MSWS), were used in order to create the predictive model. The accuracy of the model is quite well and is estimated by the value of AUC=0.72. This model shows the better predictive ability in comparison with the most widely used methods such as logistic regression, usage of decision trees, neural networks etñ.
Key words: acute coronary disease, ischemic heart disease, recognition, collective decision making, optimal partition, prognosis.
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Table of Contents
Original Article
Math. Biol. Bioinf. 2016;11(1):46-63 doi: 10.17537/2016.11.46 published in Russian Abstract (rus.) Abstract (eng.) Full text (rus., pdf) References
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