Topics: Regression analysis, Logistic regression, Blood pressure Pages: 2 (638 words) Published: May 20, 2015
Q-1A surgeon wish to predict the live or death (status) of a CABG patient through their pulse rate, systolic blood pressure, sugar level, hemoglobin etc. He got the data from last 1600 surgeries held in a local hospital and applied an analysis. He got the following result Identification:It is binary logistic regression (LOIGT)

The two post-operative status of the patients are death and alive coded by 0 and 1 respectively to use in binary logistic regression. Hosmer and Lemshow goodness of fit test sig value=0.896
The analysis is fitted that means the analysis is compatible with the data and the logit model is expected to predict the post-operative status of the patient Block 0
Accuracy =29%
This shows the fluke accuracy about the post-operative status of the patient if none of the indicators are used as predictors. Variables not in equation with sig. values
Pulse Rate0.004
Systolic BP0.000
Sugar Level0.001
The table shows that if Pulse rate, systolic BP and Sugar level are used for prediction it will help to predict the post-operative status of the patient more accurately while HB does not matter Block 1

Omnibus sig0.0000
Neglekark R square0.548Cox & Snell R-square0.458
The omnibus test is showing that the logit model is expected to have enough better accuracy then fluke while Neglekarke R square is showing that about 55% increase in accuracy is expected if we use logit model to predict post-operative status of the patient

Variables in equation with B coefficients, Sig. value and Exp (B) Pulse Rate1.2450.0041.45
Systolic BP4.6580.0001.104
Sugar Level-0.5680.0010.846
Therefor the logit model is
Link function = 1.245 (PR) + 4.658 (SBP) – 0.568 (SL)
Where, Link function = post-operative status of the patient The accuracy of above model is 78%
The Exp (B) shows that :
The chance of life increase by 45% if the Pulse rate of...
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