Tidymodels Machine Learning: Diabetes Classification

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Tidymodels Machine Learning: Diabetes Classification

9 July 2023

knitr::opts_chunk$set(message=FALSE, warning=FALSE, eval = FALSE) #set eval = TRUE when run first



dir.create("cl_diabetes", showWarnings = FALSE)



Diabetes is a leading cause of death and disability worldwide, affecting people regardless of country, age, and sex. Type 2 diabetes, which makes up most of the diabetes cases, is largely preventable and sometimes potentially reversible if identified and managed early in the disease course. (Ong et al. 2023) Hence, diagnostic routines play an important role in diabetes healthcare management. In order to be able to reliably predict the disease we first need to learn the association between risk factors and diabetes from data, and secondly, we need to provide out-of-sample predictions using new patient data. But how can we learn about the association between available features (risk factors) and diabetes? We use the tidymodels framework and apply different machine learning methods to the Kaggle diabetes data set.

Data & Modeling Strategy

The data contains 100.000 instances, the binary label diabetes, and 8 potential risk factors. We split the data into training and test partitions (75/25) before further analysis.

#download data from Kaggle and include into folder cl_diabetes
#read the data:
diabetes_df <- read_csv('cl_diabetes/diabetes_prediction_dataset.csv') %>%
  mutate(diabetes = as.factor(diabetes)) %>%
  mutate(bmi=as.numeric(case_when(bmi!='N/A' ~ bmi,
                                  TRUE ~ NA_real_))) %>%
  mutate_if(is.character, as.factor) 

# Reproducibility 

# Divide data into training and test: 
diabetes_split <- diabetes_df %>%
                initial_split(prop = 0.75, strata=diabetes)

diabetes_train_df <- training(diabetes_split)
diabetes_test_df  <-  testing(diabetes_split)

#create summary statistics:
hd_tab1 <- diabetes_train_df %>% 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                              all_categorical() ~ "{n} ({p}%)"),
              digits = all_continuous() ~ 2) %>%
    add_p(test=list(all_continuous() ~ "t.test", 
                    all_categorical() ~ "chisq.test.no.correct")) %>%
  add_overall() %>%
  modify_spanning_header(c("stat_1", "stat_2") ~ "**diabetes**") %>%
  modify_caption("**Table 1: Descriptive Statistics Training Data**") 

#save summary statistics:
hd_tab1 %>%
  as_gt() %>%
  gtsave("cl_diabetes/diabetes_tab1.png", expand = 10)

A first look at the dataset shows that all explanatory variables are associated with diabetes, as the p-values < 0.001 suggest. As expected aging and morbidities hypertension and heart disease are positively correlated with diabetes. We also see that our outcome of interest - diabetes - is very unbalanced, which we should keep in mind when selecting our evaluation metric. The feature engineering -steps are pretty straightforward. After having created the training and test partitions we normalize all numeric predictors and create dummy variables for the categorical ones. Furthermore, we down-sample the data toward the minority class.

# data preparation before training:
diabetes_recipe <-
  recipe(diabetes ~ ., data=diabetes_train_df) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_dummy(all_nominal_predictors()) %>%
  step_zv(all_predictors()) %>%

We compare different model types in our analysis because we want to select the model which predicts diabetes best. As Domingos (2012) points out, the best model depends on the use case and cannot be known in advance. Henceforth, we use XGBoost, naive Bayes, a support vector machine, a decision tree model, and logistic regression in our workflow. On the one hand, we consider logistic regression as our baseline model because it is simple and needs no additional hyperparameter tuning. On the other hand, simple decision trees have the advantage that they a highly interpretable and are therefore part of the human decision-making toolkit (eg. Wang, Luan, and Gigerenzer 2022).

# try out different ml-approaches: 
lr_mod <- logistic_reg() %>%
  set_engine("glm") %>% 

svm_mod <- svm_linear(cost = tune(), margin = tune()) %>% 
  set_engine("kernlab") %>% 

xgb_mod <- boost_tree(tree_depth = tune(), learn_rate = tune(), loss_reduction = tune(), 
             min_n = tune(), sample_size = tune(), trees = tune()) %>% 
  set_engine("xgboost") %>% 

nb_mod <- naive_Bayes(smoothness = tune(), Laplace = tune()) %>% 
  set_engine("naivebayes") %>% 

cit_mod <- decision_tree(tree_depth=tune(), min_n=tune()) %>%
  set_engine(engine = "partykit") %>%
  set_mode(mode = "classification") 

Hyperparameter Tuning

To manage the bias-variance tradeoff the model’s hyperparameters are selected by combining cross-validation using a space-filling grid search design. We consider ROC-AUC as our evaluation metric of interest. The AUC is threshold-independent and therefore often a good choice in situations when the outcome variable is relatively unbalanced. (See for example Fawcett 2006). Finally, parallelization helps speed up the whole process.

# prepare cross validation 
diabetes_train_folds <- vfold_cv(diabetes_train_df, v=8, strata = diabetes)

# prepare workflow
wf_set <- workflow_set(
    preproc = list(mod = diabetes_recipe), 
    models = list(log_reg=lr_mod, svm_linear = svm_mod, xgboost=xgb_mod, naiveBayes=nb_mod, tree=cit_mod)) 

# prepare grid: 
grid_ctrl <-
    save_pred = TRUE,
    parallel_over = "everything",
    save_workflow = TRUE , 
    event_level = "second"

# prepare parallel processing: 
cores <- parallel::detectCores(logical = TRUE)

# Create a cluster object and then register: 
cl <- makePSOCKcluster(cores) 

# Start hyperparameter tuning:
train_results <- wf_set %>%
    fn = 'tune_grid', 
    metrics = metric_set(roc_auc), 
    seed = 1503,
    resamples = diabetes_train_folds, 
    grid = 25, 
    control = grid_ctrl 


#plot results of hyperparameter tuning:
p1_diab <- train_results %>%
  autoplot() +
  theme_minimal() +
  labs(title='Figure 1: Results Hyperparameter Tuning')

ggsave(p1_diab, file="cl_diabetes/p1_diab.png")

Figure 1 shows performance metrics for all experiments ranked by AUC. We find that XGBoost gives the best results concerning AUC followed by the decision tree, naive Bayes, and logistic regression.

Further Optimization

Can we still improve on the current result? Although we considered a space-filling grid search design when tuning hyperparameters, there may be room for further improvement. Hence, we apply simulated annealing, iteratively trying out little hyperparameter changes, starting from the current best model.

xgb_results <- train_results %>% 

xgb_wf <- train_results %>% 

cl <- makePSOCKcluster(cores) 

#Increase performance with simulated annealing

xgb_sa <- xgb_wf %>%
    resamples =diabetes_train_folds,
    metrics = metric_set(roc_auc), 
    initial = xgb_results,
    iter = 40, 
    control = control_sim_anneal(verbose = TRUE, 
                                 no_improve = 10L, event_level = "second", cooling_coef = 0.1))


# save max auc:
auc_out <- xgb_sa  %>% 
  collect_metrics() %>% 
  slice_max(mean) %>%

# visualize sim annealing:
p2_diab <- autoplot(xgb_sa, type = "performance", metric = 'roc_auc') +
  geom_hline(yintercept=auc_out, linetype="dashed", color = 'red') +
  labs(title='Figure 2: Performance Improvement by Simulated Annealing ') +

ggsave(p2_diab, file="cl_diabetes/p2_diab.png")

The results in Figure 2 show that we only did achieve minimal improvements compared to the current best model (AUC of 0.978).

Interpretable Machine Learning

One important aspect of ML classifiers is that models with high predictive power do not necessarily have good explanatory power (eg. Shmueli 2010). In order to generate actionable insights it may nevertheless be very helpful to understand how the classifier comes to a certain conclusion. This is especially important in the medical field. Hence, interpretable machine learning can be an important driver for building trust in data-driven technologies. For example, the variable importance plot in Figure 3 shows that the variables HbA1c-level, blood glucose, age, and BMI are major factors that contribute to the diabetes-model fit.

# extract model fit after simulated annealing: 
xgb_fit <- xgb_sa %>% 
  extract_workflow() %>%
  finalize_workflow(xgb_sa %>% select_best())  %>%
  fit(data = diabetes_train_df) %>%

# Variable importance plot:
p3_diab <- xgb_fit %>%
  vip() +
  theme_minimal() +
  labs(title="Figure 3: Variable Importance")

ggsave(p3_diab, file="cl_diabetes/p3_diab.png")

A look at partial dependence plots reveals the conditional impact of our explanatory variables on the predictions:

#prepare training data for pdp:
xgb_df <- xgb_sa %>% 
  extract_workflow() %>%
  finalize_workflow(xgb_sa %>% select_best())  %>%
  fit(data = diabetes_train_df) %>%
  extract_recipe() %>%

explain_xgb <- explain_tidymodels(
                data = (xgb_df %>% dplyr::select(-diabetes)), #data without target column
                y = xgb_df$diabetes,
                label = "xgboost",
                verbose = FALSE

# create model profile:
pdp_diab  <- model_profile(explain_xgb, N=1000, variables = "HbA1c_level", groups='hypertension') 

#Create ggplot manually for HbA1c, grouped by hypertension:
p4_diab <- pdp_diab$agr_profiles %>% 
  as_tibble() %>%
  mutate(RiskFactor=paste0('hypertension=', ifelse(stringr::str_sub(`_label_`, 9, 9)=='-', '0', '1'))) %>%
  ggplot(aes(x=`_x_`, y=`_yhat_`, color=RiskFactor)) +
  geom_line(linewidth=2) +
  labs(y='Diabetes Risk Score', x='HbA1c_level', title='Figure 4: Partial Dependence Plot') +

ggsave(p4_diab, file="cl_diabetes/p4_diab.png")

For example, in Figure 4 we see that the conditional association between HbA1c level and diabetes is positive, and even more pronounced for people with hypertension, which makes sense as hypertension is another known risk factor for diabetes. At this point, we should also remember that our predictions cannot be interpreted as probabilities (Van Calster et al. 2019). If we would like the predicted values to equal expected probabilities we would need to recalibrate the risk scores or use another modeling strategy.


Now, as we have developed a final model, we will test whether our predictions do also perform well on new unseen data. Hence we will test the model performance on our hold-out data partition. As usual, the confusion matrix is based on a risk-score cutoff of 0.5. The AUC of 0.98 on the test data confirms that the model generalizes well on unseen data.

# Fit new best model once on test data at the final end of the process: 
test_results <- xgb_sa %>%
  extract_workflow() %>%
  finalize_workflow(xgb_sa %>% select_best()) %>% 
  last_fit(split = diabetes_split)

# create predictions:
test_p <- collect_predictions(test_results)

# confusion matrix:
conf_mat <- conf_mat(test_p, diabetes, .pred_class) 

p5a_diab <- conf_mat %>%
  autoplot(type = "heatmap") +
  theme(legend.position = "none") +
  labs(title='Confusion Matrix')

#AUC overall
auc <- test_p %>%
  roc_auc(diabetes, .pred_1, event_level = "second") %>%
  mutate(.estimate=round(.estimate, 3)) %>%

roc_curve <- roc_curve(test_p, diabetes, .pred_1, event_level = "second") 

p5b_diab <- roc_curve %>%
  autoplot() +
  annotate('text', x = 0.3, y = 0.75, label = auc) +
  theme_minimal() +
#combine both plots:
p5_diab <- p5a_diab + p5b_diab + plot_annotation('Figure 5: Evaluation on Test Data')

ggsave(p5_diab, file="cl_diabetes/p5_diab.png")


We did a comparative analysis of different machine learning classifiers to predict diabetes with some medical risk factors using the tidymodels framework. Then we showed how to further improve the best model (in our case XGBoost) using iterative grid search before validating the model on test data. In addition, we applied visualization methods to better understand how the predictions are generated. Overall, we see that machine learning methods can fruitfully support medical decision-making, if the model is well developed, validated, and integrated carefully into the application of interest.


Domingos, Pedro. 2012. “A Few Useful Things to Know about Machine Learning.” Communications of the ACM 55 (10): 78–87.
Fawcett, Tom. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters 27 (8): 861–74.
Kuhn, Max, and Julia Silge. 2022. Tidy Modeling with r. " O’Reilly Media, Inc.".
Kuhn, Max, and Hadley Wickham. 2020. Tidymodels: A Collection of Packages for Modeling and Machine Learning Using Tidyverse Principles. https://www.tidymodels.org.
Maksymiuk, Szymon, Alicja Gosiewska, and Przemyslaw Biecek. 2020. “Landscape of r Packages for eXplainable Artificial Intelligence.” arXiv. https://arxiv.org/abs/2009.13248.
Ong, Kanyin Liane, Lauryn K Stafford, Susan A McLaughlin, Edward J Boyko, Stein Emil Vollset, Amanda E Smith, Bronte E Dalton, et al. 2023. “Global, Regional, and National Burden of Diabetes from 1990 to 2021, with Projections of Prevalence to 2050: A Systematic Analysis for the Global Burden of Disease Study 2021.” The Lancet.
Shmueli, Galit. 2010. “To Explain or to Predict?” Statistical Science 25 (3): 289–310.
Van Calster, Ben, David J McLernon, Maarten Van Smeden, Laure Wynants, and Ewout W Steyerberg. 2019. “Calibration: The Achilles Heel of Predictive Analytics.” BMC Medicine 17 (1): 1–7.
Wang, Yuhui, Shenghua Luan, and Gerd Gigerenzer. 2022. “Modeling Fast-and-Frugal Heuristics.” PsyCh Journal 11 (4): 600–611.
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