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Predicting Threat Degree for Onset of Type 2 Diabetes Mellitus Based on Machine Learning Methods
Shakhmametova, G., Vakkazov, N., Klimets, S. (2023)
classification model based on the logistic regression method has been proposed and developed. The developed
Risk Factors for Cerebral Hyperperfusion Syndrome After Combined Revascularization in Adult Patients with Moyamoya Disease
Xu, Dongxiao, Guo, Jiaojiao, Zheng, Bingjie, Wu, Qiaowei, Gareev, Ilgiz, Beylerli, Ozal, Beilerli, Aferin, Shi, Huaizhang (2023)
features were recorded. Postoperative CHS after surgery were examined. Multivariate logistic regression
Polygenic Score Approach to Predicting Risk of Metabolic Syndrome
Timasheva, Yanina, Kochetova, Olga, Balkhiyarova, Zhanna, Korytina, Gulnaz, Prokopenko, Inga, Nouwen, Arie (2025)
variants across these pathways in 279 MetS patients and 397 healthy individuals. Using logistic regression
LEFT VENTRICULAR EJECTION FRACTION AS AN INDEPENDENT PREDICTOR OF POOR OUTCOME IN ACUTE INTRACEREBRAL HEMORRHAGE
YANG, G., SUN, J., ZHANG, D., ZHANG, R., ZHONG, Y., WANG, X., CHEN, X., LIU, B., LI, L., ZHAO, S., WANG, L., YUAN, C., LONG, M., JIANG, H., LI, C., ZHOU, Q., LIAN, A., GAREEV, I. (2020)
were investigated using univariable and multivariable logistic regression models. Results: Depressed
IREB2, CHRNA5, CHRNA3, FAM13A & HEDGEHOG INTERACTING PROTEIN GENES POLYMORPHISMS & RISK OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN TATAR POPULATION FROM RUSSIA
Korytina, G.F., Akhmadishina, L.Z., Viktorova, E.V., Kochetova, O.V., Viktorova, T.V. (2016)
). Logistic regression was used to detect the association of SNPs and haplotypes of linked loci in different
Predictors of Adverse Outcomes in Ischemic Stroke Associated with COVID-19
Novikova, L.B. ;, Latypova, R.F. (2023)
outcome were studied by binary logistic regression. Results. Univariate analysis showed that the risk
ПРОГНОЗИРОВАНИЕ РЕЗУЛЬТАТОВ ЛЕЧЕНИЯ ПЕРВИЧНОГО ПУЗЫРНО-МОЧЕТОЧНИКОВОГО РЕФЛЮКСА У ДЕТЕЙ
PREDICTING THE RESULTS OF TREATMENT OF PRIMARY VESICOURETERAL REFLUX IN CHILDREN
Зоркин, С.Н., Гурская, А.С., Баязитов, Р.Р., Шахновский, Д.С., Zorkin, S.N., Gurskaya, A.S., Baiazitov, R.R., Shakhnovskiy, D.S. (2022)
PREVALENCE AND RISK FACTORS FOR ERECTILE DYSFUNCTION AND LOWER URINARY TRACT SYMPTOMS IN RUSSIAN FEDERATION MEN: ANALYSIS FROM A NATIONAL POPULATION-BASED MULTICENTER STUDY
Korneyev, I.A., Alexeeva, T.A., Al-Shukri, S.H., Bernikov, A.N., Erkovich, A.A., Kamalov, A.A., Kogan, M.I., Pavlov, V.N., Zhuravlev, V.N., Pushkar, D.Y. (2016)
-adjusted odds ratio between 1.2 and 5.2. In logistic regression model (R2=0.361), the strongest associated
Multilocus evaluation of genetic predictors of multiple sclerosis
Timasheva, Y, Nasibullin, TR, Tuktarova, IA, Erdman, VV, Galiullin, TR, Zaplakhova, OV, Bakhtiiarova, KZ (2022)
tested using logistic regression analysis under additive genetic model adjusted for sex. Meta
ANTISOCIAL BEHAVIOR: NATURE VS. NURTURE
Kazantseva, A.V., Yakovleva, D.V., Davydova, Yu. D., Kosareva, A.R., Valinurov, R.G., Khusnutdinova, E.K. (2023)
). A series of logistic regression (PLINK v.1.09) confirmed the association of REV3L rs458806

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