R/model_cardio.R

In robertoromor/heartdisease: Heart Disease Probability Calculator

#' Heart Disease Probability Function
#'
#' @param age age of person as of today
#' @param sex Female or Male
#' @param thalach  blood pressure
#'
#' @return The odds of having a hear disease
#' @export
#'
#' @examples
#' model_cardio(30, "Female" , 120)
#' \dontrun{
#' model_cardio(52, "Male" , 120)
#' }
model_cardio<-function(age, sex, thalach){

  hd_data<-read.csv("./heart_data.csv", sep=",")

#  hd_data %>% mutate(hd = ifelse(class > 0, 1, 0))-> hd_data
#
#  # recode sex using mutate function and save as hd_data
#  hd_data %>% mutate(sex = factor(sex, levels = 0:1, labels = c("Female","Male")))-> hd_data
#
#
#
#  model <- glm(data = hd_data, hd~age+sex+thalach, family="binomial")
#
#
#  # tidy up the coefficient table
#  tidy_m <- model %>% tidy()
#  tidy_m
#
#  # calculate OR
#  tidy_m$OR <- exp(tidy_m$estimate)
#
#  # calculate 95% CI and save as lower CI and upper CI
#  tidy_m$lower_CI <- exp(tidy_m$estimate - 1.96 * tidy_m$std.error)
#  tidy_m$upper_CI <- exp(tidy_m$estimate + 1.96 * tidy_m$std.error)
#
#
#
#  # get the predicted probability in our dataset using the predict() function
#  pred_prob <- predict(model, hd_data, type = "response")
#
#  # create a decision rule using probability 0.5 as cutoff and save the predicted decision into the main data frame
#  hd_data$pred_hd <- ifelse(pred_prob>=.5,1,0)
#
#  # create a newdata data frame to save a new case information
#  newdata <- data.frame(age, sex, thalach)
#
#  # predict probability for this new case and print out the predicted value
#  p_new <- predict(model, newdata, type = "response")
#
#
#  return(p_new)
#

return(hd_data)

}


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#
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