How to implement Kernel Density Estimation with Multivariate Data for abnormal detection in R?

I tried to implement Kernel Density Estimation with Multivariate data in R, but it is first time for me, so, I'm not sure is it correct approach or not.

I got approach as like below with 176 obs. of 6 variables data.

# data structure as like below. 
# If num is 0 is normal, if num is 1, it would be outlier.

str(df)

'data.frame':   176 obs. of  6 variables:
 $ age     : int  30 50 50 50 50 60 50 40 50 40 ...
 $ trestbps: int  130 130 130 130 130 130 130 130 130 130 ...
 $ chol    : int  198 245 221 288 205 309 240 243 289 250 ...
 $ thalach : int  130 166 164 159 184 131 154 152 124 179 ...
 $ oldpeak : num  1.6 2.4 0 0.2 0 1.8 0.6 0 1 0 ...
 $ num     : int  0 0 0 0 0 0 0 0 0 0 ...

Because it is multivariate data, I got approach as like below.

#6th column is the answer of outlier, So do not use it.

df <- transform(df, age_s = scale(df$age), trestbps_s = scale(df$trestbps), chol_s = scale(df$chol), thalach_s = scale(df$thalach), oldpeak_s = scale(df$oldpeak))


df1 <- df[-c(1,2,3,4,5,6)]
df1 <- as.matrix(df1)

kde_df1 <- density(df1, n = 176)
kde_df1

plot(kde_df1)
densities <- as.data.frame(kde_df1$y)

names(densities) <- c("dy")

min_density <- min(densities$dy)
mean_density <- mean(densities$dy)
bench <- min_density/mean_density


> bench
[1] 0.0002983908606
> summary(densities$dy)
    Min.      1st Qu.       Median         Mean      3rd Qu. 
0.0000298944 0.0029706735 0.0217674956 0.1001852137 0.1766697744 
    Max. 
0.4760445127 


# Predict Outliers
densities$outlier <- NA

densities$outlier <- ifelse(densities$dy < bench, "1", "0")

densities1 <- densities

densities1$outlier <- as.factor(densities1$outlier)
levels(densities1$outlier)

> summary(densities1$outlier)
  0   1 
169   7 


> densities1$outlier
  [1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [73] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
Levels: 0 1

Try to check Confusion Matrix

library(caret)

dfAnswer <- df
dfAnswer$num <- as.factor(dfAnswer$num)

CM <- confusionMatrix(densities1$outlier, dfAnswer$num)

> CM
Confusion Matrix and Statistics

      Reference
Prediction   0   1
         0 157  12
         1   3   4
                                            
           Accuracy : 0.9147727             
             95% CI : (0.8633274, 0.9515133)
No Information Rate : 0.9090909             
P-Value [Acc > NIR] : 0.4619822             
                                            
              Kappa : 0.3096234             
                                            
 Mcnemar's Test P-Value : 0.0388671             
                                            
        Sensitivity : 0.9812500             
        Specificity : 0.2500000             
     Pos Pred Value : 0.9289941             
     Neg Pred Value : 0.5714286             
         Prevalence : 0.9090909             
     Detection Rate : 0.8920455             
   Detection Prevalence : 0.9602273             
      Balanced Accuracy : 0.6156250             
                                            
   'Positive' Class : 0                     
                          

Am I doing right? If it is not, can you give me some advices to get approach correctly?

Thank you.