R/model_cardio.R

In Yahirds/heartbreak: Heart Break

#' Model Cardio Function
#'
#' @param age edad del paciente
#' @param sex sexo del paciente ("Male","Female")
#' @param thalach presion sanguinea del paciente
#'
#' @return
#' @export
#'
#' @examples
#' model_cardio(25,"Male",100)
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)

}