YANG Qingsheng, YOU Xibin, ZHANG Hongxian, Kevin MWENDA, WANG Yuandong, HUANG Ying. A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China[J]. Chinese Geographical Science, 2020, 30(1): 157-169. doi: 10.1007/s11769-020-1102-7
Citation: YANG Qingsheng, YOU Xibin, ZHANG Hongxian, Kevin MWENDA, WANG Yuandong, HUANG Ying. A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China[J]. Chinese Geographical Science, 2020, 30(1): 157-169. doi: 10.1007/s11769-020-1102-7

A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China

doi: 10.1007/s11769-020-1102-7
Funds:

Under the auspices of National Natural Science Foundation of China (No. 40971060)

  • Received Date: 2018-12-06
  • Rev Recd Date: 2019-02-22
  • Reference values of erythrocyte sedimentation rate (ESR) are the key to interpret ESR blood test in clinic. The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression (MLR) model and the artificial neural network (ANN). These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited. This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning (CBR) since knowledge domain of ESR and geographical factors is weak. Overall 224 local normal ESR values of China that calculated from 13 623 samples were obtained, and the corresponding natural geographical factors and location that include altitude, sunshine hours, relative humidity, temperature, precipitation, annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China. CBR was used to predict the unseen local reference ESR values with cases. The average absolute deviation (AAD), mean square error (MSE), prediction accuracy (PA), and Pearson correlation coefficient (r) between the observed and estimated data of proposed model is 33.07%, 9.02, 66.93% and 0.78, which are better than those of ANN and MLR model. The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models. The predicted values are very close to the observed values. Model results show significant agreement of cases data. Consequently, the model is used to predict the unseen local reference ESR with natural geographical factors and location. In spatial, the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan, Chongqing, Guangxi and Guizhou provinces, and the reference ESR values are greater than 23 mm/60 min. The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei, Henan, Shaanxi, Shanxi, Jilin and Heilongjiang provinces, and the reference ESR values are greater than 18 mm/60 min. The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang, and the reference ESR values are lower than 5 mm/60 min.
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A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China

doi: 10.1007/s11769-020-1102-7
Funds:

Under the auspices of National Natural Science Foundation of China (No. 40971060)

Abstract: Reference values of erythrocyte sedimentation rate (ESR) are the key to interpret ESR blood test in clinic. The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression (MLR) model and the artificial neural network (ANN). These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited. This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning (CBR) since knowledge domain of ESR and geographical factors is weak. Overall 224 local normal ESR values of China that calculated from 13 623 samples were obtained, and the corresponding natural geographical factors and location that include altitude, sunshine hours, relative humidity, temperature, precipitation, annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China. CBR was used to predict the unseen local reference ESR values with cases. The average absolute deviation (AAD), mean square error (MSE), prediction accuracy (PA), and Pearson correlation coefficient (r) between the observed and estimated data of proposed model is 33.07%, 9.02, 66.93% and 0.78, which are better than those of ANN and MLR model. The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models. The predicted values are very close to the observed values. Model results show significant agreement of cases data. Consequently, the model is used to predict the unseen local reference ESR with natural geographical factors and location. In spatial, the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan, Chongqing, Guangxi and Guizhou provinces, and the reference ESR values are greater than 23 mm/60 min. The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei, Henan, Shaanxi, Shanxi, Jilin and Heilongjiang provinces, and the reference ESR values are greater than 18 mm/60 min. The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang, and the reference ESR values are lower than 5 mm/60 min.

YANG Qingsheng, YOU Xibin, ZHANG Hongxian, Kevin MWENDA, WANG Yuandong, HUANG Ying. A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China[J]. Chinese Geographical Science, 2020, 30(1): 157-169. doi: 10.1007/s11769-020-1102-7
Citation: YANG Qingsheng, YOU Xibin, ZHANG Hongxian, Kevin MWENDA, WANG Yuandong, HUANG Ying. A New Method to Predict Erythrocyte Sedimentation Rate with Natural Geographical Factors and Location by Case-based Reasoning: A Case Study of China[J]. Chinese Geographical Science, 2020, 30(1): 157-169. doi: 10.1007/s11769-020-1102-7
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