User

I uploaded the file: lin_chuang_jing_ji_xue_shu_ju.xlsx

please provide some insights of the data

Julius

I’ll analyze the Excel file you’ve uploaded and provide some insights. Let me first load the data and take a look at what we’re working with.

run_r_code

# Load necessary libraries
library(readxl)

# Read the Excel file
data <- read_excel("lin_chuang_jing_ji_xue_shu_ju.xlsx", sheet = "Sheet1")

# Check the structure of the data
str(data)

# Display the first few rows
head(data)

# Get summary statistics
summary(data)

# Check dimensions of the dataset
dim(data)

500

6

Here are some initial insights based on the data from the Excel file:

The summary statistics ([]) indicate that some columns (like AGE) might be heavily skewed or have a concentration at lower values. Further analysis could drill into relationships among these variables, such as examining correlations between length of stay or total charges based on age or diagnosis codes.

Let me know if you’d like any additional visualizations or a deeper dive into any of these aspects.

User

Please generate a linear model for predicting ‘Total Charges’ using the other information.

Julius

run_r_code

# Build a linear model for predicting 'Total Charges' using the other columns
# Remove any rows with missing values in the predictor 'Length of Stay'
data_clean <- na.omit(data)
model <- lm(`Total Charges` ~ AGE + FEMALE + `Length of Stay` + RACE + `Diagnosis Code`, data = data_clean)

# Summarize the model
summary(model)

# Print the first few coefficients
head(coef(model))

(Intercept)

5218.67694369858

AGE

134.694873194232

FEMALE

-390.69243229903

Length of Stay

743.15207225637

RACE

-212.429056722503

Diagnosis Code

-7.79094810662535

Below is the summary of the linear model we generated for predicting Total Charges using predictor variables AGE, FEMALE, Length of Stay, RACE, and Diagnosis Code. The output shows that:

The overall model has an

value of approximately 0.5536, meaning that roughly 55.36% of the variability in Total Charges is explained by the model. Further diagnostics might be useful to check assumptions or potential outliers.

Key outputs:

Call: lm(formula = Total Charges ~ AGE + FEMALE + Length of Stay + RACE + Diagnosis Code, data = data_clean)

Residuals: Min 1Q Median 3Q Max -6377 -700 -174 122 43378

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 5218.6769 507.6475 10.280 < 2e-16 AGE 134.6949 17.4711 7.710 7.02e-14 FEMALE -390.6924 247.7390 -1.577 0.115
Length of Stay 743.1521 34.9225 21.280 < 2e-16 RACE -212.4291 227.9326 -0.932 0.352
Diagnosis Code -7.7909 0.6816 -11.430 < 2e-16 — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2613 on 493 degrees of freedom Multiple R-squared: 0.5536, Adjusted R-squared: 0.5491 F-statistic: 122.3 on 5 and 493 DF, p-value: < 2.2e-16

Let me know if you’d like to explore any other aspects or diagnostics of the model!

User

Please convert RACE into dummy variables.

Julius

run_r_code

# Convert RACE into a factor (dummy variables) in the cleaned data
data_clean$RACE <- as.factor(data_clean$RACE)

# Build the linear model with dummy variables for RACE
model_dummy <- lm(`Total Charges` ~ AGE + FEMALE + `Length of Stay` + RACE + `Diagnosis Code`, data = data_clean)

# Summarize the new model
summary(model_dummy)

# Show the coefficients for RACE dummy variables
coef(summary(model_dummy))

run_r_code

# Let's check the distribution of RACE values in our dataset
table(data_clean$RACE)

# Get the full coefficient table with names
coef_table <- coef(model_dummy)
print(coef_table)

I’ve converted the RACE variable into dummy variables and rebuilt the linear model. Here’s what the updated model shows:

Call: lm(formula = Total Charges ~ AGE + FEMALE + Length of Stay + RACE + Diagnosis Code, data = data_clean)

Residuals: Min 1Q Median 3Q Max -6367 -691 -186 121 43412

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 5024.9610 440.1366 11.417 < 2e-16 AGE 133.2207 17.6662 7.541 2.29e-13 FEMALE -392.5778 249.2981 -1.575 0.116
Length of Stay 742.9637 35.0464 21.199 < 2e-16 RACE2 458.2427 1085.2320 0.422 0.673
RACE3 330.5184 2629.5121 0.126 0.900
RACE4 -499.3818 1520.9293 -0.328 0.743
RACE5 -1784.5776 1532.0048 -1.165 0.245
RACE6 -594.2921 1859.1271 -0.320 0.749
Diagnosis Code -7.8175 0.6881 -11.361 < 2e-16 — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2622 on 489 degrees of freedom Multiple R-squared: 0.5544, Adjusted R-squared: 0.5462 F-statistic: 67.6 on 9 and 489 DF, p-value: < 2.2e-16

Looking at the distribution of RACE values in our dataset:

1 2 3 4 5 6 484 6 1 3 3 2

The RACE variable is highly imbalanced, with 484 observations (about 97%) belonging to category 1, which serves as the reference level in our model. The other categories (2-6) have very few observations.

The model coefficients are:

(Intercept) AGE FEMALE Length of Stay 5024.960965 133.220679 -392.577823 742.963675 RACE2 RACE3 RACE4 RACE5 458.242745 330.518450 -499.381799 -1784.577602 RACE6 Diagnosis Code -594.292150 -7.817502

Key observations from the model with RACE dummy variables:

  1. The model’s R-squared is slightly improved to 0.5544 (from 0.5536), but the adjusted R-squared decreased to 0.5462 (from 0.5491) due to the additional parameters.

  2. None of the RACE dummy variables (RACE2 through RACE6) are statistically significant, likely due to the small number of observations in these categories.

  3. The coefficients for the other variables remain similar to the previous model:

  1. The intercept (5024.96) represents the baseline Total Charges for a patient with RACE=1, AGE=0, FEMALE=0, Length of Stay=0, and Diagnosis Code=0.

Given the imbalance in the RACE variable, the dummy variables don’t add much predictive power to the model. The most significant predictors remain Length of Stay, AGE, and Diagnosis Code.

User

Diagnosis Code should be dummy variables too

Julius

You’re right. Let’s convert the Diagnosis Code into dummy variables as well.

run_r_code

# First, let's check how many unique Diagnosis Codes we have
length(unique(data_clean$`Diagnosis Code`))

# Let's see the distribution of Diagnosis Codes
table_diag <- table(data_clean$`Diagnosis Code`)
head(sort(table_diag, decreasing = TRUE), 10) # Top 10 most frequent codes

63 ### run_r_code

# Since we have 63 unique diagnosis codes, creating dummy variables for all of them
# might lead to overfitting. Let's consider a few approaches:

# 1. First, let's look at the top diagnosis codes more carefully
top_diag <- names(sort(table(data_clean$`Diagnosis Code`), decreasing = TRUE)[1:10])
print(top_diag)

# 2. Let's create a new variable for the most common diagnosis code (640)
data_clean$DiagCode_640 <- ifelse(data_clean$`Diagnosis Code` == 640, 1, 0)

# 3. Let's also create a categorical variable for the top 5 diagnosis codes
data_clean$DiagCode_Cat <- "Other"
data_clean$DiagCode_Cat[data_clean$`Diagnosis Code` == 640] <- "640"
data_clean$DiagCode_Cat[data_clean$`Diagnosis Code` == 754] <- "754"
data_clean$DiagCode_Cat[data_clean$`Diagnosis Code` == 753] <- "753"
data_clean$DiagCode_Cat[data_clean$`Diagnosis Code` == 758] <- "758"
data_clean$DiagCode_Cat[data_clean$`Diagnosis Code` == 751] <- "751"
data_clean$DiagCode_Cat <- as.factor(data_clean$DiagCode_Cat)

# Check the distribution of our new categorical variable
table(data_clean$DiagCode_Cat)

I’ve created a new categorical variable for Diagnosis Code, grouping the top five most frequent codes (640, 751, 753, 754, 758) and labeling the remaining codes as “Other”. Here are the outputs:

[1] “640” “754” “753” “758” “751” “755” “53” “249” “626” “139”

and the distribution of the new variable:

640 751 753 754 758 Other 266 14 36 37 20 126

Now, I’ll build a linear model with both RACE (as dummy variables) and Diagnosis Code (using the new categorical variable).

User

please build a linear model with both RACE (as dummy variables) and Diagnosis Code (using the new categorical variable).

Julius

run_r_code

# Building the new linear model with both RACE (as dummy variables) and Diagnosis Code categorical variable

# Ensure RACE is factor and the new Diagnosis variable is factor

data_clean$RACE <- as.factor(data_clean$RACE)

data_clean$DiagCode_Cat <- as.factor(data_clean$DiagCode_Cat)

# Build the linear model
model_final <- lm(`Total Charges` ~ AGE + FEMALE + `Length of Stay` + RACE + DiagCode_Cat, data = data_clean)

# Summarize the new model
summary(model_final)

# Print the coefficients
coef(model_final)

(Intercept)

194.314017286165

AGE

173.812611802842

FEMALE

-569.519824109169

Length of Stay

692.138732942433

RACE2

1319.07566013832

RACE3

-1435.7834034598

RACE4

178.021984111057

RACE5

-1635.35909501744

RACE6

-188.503058287234

DiagCode_Cat751

-2293.65881450389

DiagCode_Cat753

-2288.1215774293

DiagCode_Cat754

-2270.17462664509

DiagCode_Cat758

-2538.24065158231

DiagCode_CatOther

2083.43427851307

Here is the summary of the new linear model:

Call: lm(formula = Total Charges ~ AGE + FEMALE + Length of Stay + RACE + DiagCode_Cat, data = data_clean)

Residuals: Min 1Q Median 3Q Max -5637 -425 -80 245 38880

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 194.31 209.72 0.927 0.354622
AGE 173.81 34.32 5.064 5.83e-07 FEMALE -569.52 245.93 -2.316 0.020988
Length of Stay 692.14 36.57 18.925 < 2e-16 RACE2 1319.08 1091.22 1.209 0.227324
RACE3 -1435.78 2635.30 -0.545 0.586123
RACE4 178.02 1519.94 0.117 0.906810
RACE5 -1635.36 1554.17 -1.052 0.293213
RACE6 -188.50 1879.13 -0.100 0.920137
DiagCode_Cat751 -2293.66 891.22 -2.574 0.010360
DiagCode_Cat753 -2288.12 685.45 -3.338 0.000908 DiagCode_Cat754 -2270.17 679.89 -3.339 0.000906 DiagCode_Cat758 -2538.24 797.77 -3.182 0.001558 ** DiagCode_CatOther 2083.43 407.09 5.118 4.46e-07 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2606 on 485 degrees of freedom Multiple R-squared: 0.5633, Adjusted R-squared: 0.5515 F-statistic: 48.11 on 13 and 485 DF, p-value: < 2.2e-16

The coefficients for the predictors (including RACE dummies and the new Diagnosis Code categorical variable) are:

{outputs_dict[‘html_elements’][0]}

Key takeaways from the model include:

Let me know if you’d like further exploration or modifications.