I wondered what variables affect the patients in the ICU.

The purpose of this study is to determine the survival of patients who were admitted to the ICU.

The key points of this study is to find out what independent variablesinfluence the dependent variable based on the results.

It also proceeds to test whether the actual patients died or not.

• 200 observations

• 1 dependent Variables

• 8 independent variables

Dependent variable

STA : Status of patients (0=dead, 1=alive)

Independent variables

AGE : Age of patients

SEX : Gender of patients(0=female,1=male)

RACE : Race of patients(There are 3 types of race.)

SER : Experience of ICU as patients(0=No, 1=Yes)

CAN : Cancer (0=No cancer, 1=Cancer)

INF : Infection (0=No, 1=Yes)

CPR : CPR before arrive at ICU (0=No, 1=Yes)

HRA : Heart rate

Logistic Linear RegressionLogistic regression analysis was used as the main research method.

Two types of variable selection

1. Forward selection2. P-value selection

Hosmer & Lemeshow TestHosmer & Lemeshow test was conducted as fitness check.

ID STA AGE SEX RACE SER CAN INF CPR HRA

min 4.00 0.0 16.000 0.00 1.000 0.000 0.0 0.00 0.000 39.000

qt1st.25% 210.25 0.0 46.750 0.00 1.000 0.000 0.0 0.00 0.000 80.000

median 412.50 0.0 63.000 0.00 1.000 1.000 0.0 0.00 0.000 96.000

Mean 444.82 0.2 57.545 0.38 1.175 0.535 0.1 0.42 0.065 98.925

qt3st.75% 671.75 0.0 72.000 1.00 1.000 1.000 0.0 1.00 0.000 118.250

max 929.00 1.0 92.000 1.00 3.000 1.000 1.0 1.00 1.000 192.000

range 925.00 1.0 76.000 1.00 2.000 1.000 1.0 1.00 1.000 153.000

Examining the basic statistics before preparing data.

• AGE, HRA Histogram & plot

Checking missing value

As Amelia package, checking missing value. There is no missing value. If there are missing values, it mislead output conclusion.

Checking abnormal variables

I also checked abnormal variables as boxplot. There is no abnormal variables in AGE boxplot. In case of HRA boxplot, there is one abnormal variable.

Dummy variables, Factor

I made dummy variable for RACE because there are 3 types of RACE and changed SEX, SER, CAN, INF, CPR to factor variables.

Forward selection

First, used forward selection for making a model. CPR, AGE, SER, INF showed the lowest AIC value.

P-value selection

I also selected variables as P-value selection. In 0.05 significant level, AGE, SER, CPR was significant.

Odd Ratio

Odd ratio is used mainly in logistic regression analysis as a way of analyzing how the occurrence or absence of event A strongly affects the occurrence or absence of event B.

Hosmer & Lemeshow test

To check the fitness of the model, a Resource Selection package was installed and Hosmer & Lemeshow test was conducted.

Because the p-value is very high, the null hypothesis is adopted. Therefore, it can be said that it is a suitable model.

After eliminating variables, I tested it again

After eliminating the insignificant variables, I tried that the AGE, SER, and CPR variables were still significant.

To do this, I set the new data to Logit.model2 and set its parameter to para1. Then, Odd ratio was obtained and the hosmer test was performed.

Forward selection accuracy

P-value selection accuracy

The model was constructed through the forward selection and using the P-value. Both methods showed accuracy over 80%.

This study has meaning in that the survivability of patients in the ICU will be increased by controlling influential variables.

This study has limitations in that it has past data and has not proceededto a time series method.

Thank you