The relationship between left ventricle myocardial performance index of healthy women and geographical factors.

Journal: International journal of biometeorology
Published Date:

Abstract

The study focused on the relationship between geographical factors and left ventricular myocardial performance index (MPI)reference value, analyed the different distribution of MPI, and then provided a scientific basis for clinical examination. This study collected MPI reference values of 2545 healthy women from 91 cities in China, used the Moran's index to determin the spatial relationship, selected 25 geographical factors, examined the significance between MPI and geographical factors by correlation analysis, through the significance test, and extracted seven significant factors to build the artificial neural network (ANN) model and principal component analysis (PCA) model. Through calculating the relative error, the ANN model was chosen as the better model to predict the values. By normality test for the predicted values, the geographical distribution was made by disjunctive kriging interpolation. The predicted values decrease from north to south. If geographical factors are obtained in one location, the MPI of healthy women in this area can be predicted by the ANN model. Synthesizing the influence of physiological and geographical could be more scientific to formulate the MPI reference value.

Authors

  • Xiao Han
    College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Provincial Key Laboratory of Clean Production of Fine Chemicals, Shandong Normal University Jinan 250014 China cyzhang@sdnu.edu.cn.
  • Miao Ge
    College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China. gemiao@snnu.edu.cn.
  • Jie Dong
    Department of Urology, Eastern Theater Command General Hospital, Nanjing,Jiangsu 210002, Chinia.
  • Zixuan Wang
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
  • Jinwei He
    College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China.
  • Rongrong Yang
    College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China.