# Thyroid disease dataset The dataset is to be used with a supervised classification ML model to classify thyroid disease. The dataset was sourced and adapted from the UCI Machine Learning repository [https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php). data ## Format A data frame with 3772 rows and 28 variables: - **ThryroidClass**: binary classification label indicating whether sick = 1 or negative=0 - **patient_age**: age of the patient - **patient_gender**: flag indicating gender of patient - 1=Female and 0=Male - **presc_thyroxine**: flag to indicate whether thyroxine replacement prescribed 1=Thyroxine prescribed - **queried_why_on_thyroxine**: flag to indicate query has been actioned - **presc_anthyroid_meds**: flag to indicate whether anti-thyroid medicine has been prescribed - **sick**: flag to indicate sickness due to thyroxine depletion or over activity - **pregnant**: flag to indicate whether the patient is pregnant - **thyroid_surgery**: flag to indicate whether the patient has had thyroid surgery - **radioactive_iodine_therapyI131**: indicates whether patient has had radioactive iodine treatment: [https://www.nhs.uk/conditions/thyroid-cancer/treatment/](https://www.nhs.uk/conditions/thyroid-cancer/treatment/) - **query_hypothyroid**: flag to indicate under active thyroid query [https://www.nhs.uk/conditions/underactive-thyroid-hypothyroidism/](https://www.nhs.uk/conditions/underactive-thyroid-hypothyroidism/) - **query_hyperthyroid**: flag to indicate over active thyroid query [https://www.nhs.uk/conditions/overactive-thyroid-hyperthyroidism/](https://www.nhs.uk/conditions/overactive-thyroid-hyperthyroidism/) - **lithium**: Lithium carbonate administered to decrease the level of thyroid hormones - **goitre**: flag to indicate swelling of the thyroid gland [https://www.nhs.uk/conditions/goitre/](https://www.nhs.uk/conditions/goitre/) - **tumor**: flag to indicate a tumor - **hypopituitarism**: flag to indicate a diagnosed under active thyroid - **psych_condition**: indicates whether a patient has a psychological condition - **TSH_measured**: a TSH level lower than normal indicates there is usually more than enough thyroid hormone in the body and may indicate hyperthyroidism - **TSH_reading**: the reading result of the TSH blood test - **T3_measured**: linked to TSH reading - when free triiodothyronine rise above normal this indicates hyperthyroidism - **T3_reading**: the reading result of the T3 blood test looking for above normal levels of free triiodothyronine - **T4_measured**: free thyroxine, also known as T4, is used with T3 and TSH tests to diagnose hyperthyroidism - **T4_reading**: the reading result of th T4 test - **thyrox_util_rate_T4U_measured**: flag indicating the thyroxine utilisation rate [https://pubmed.ncbi.nlm.nih.gov/1685967/](https://pubmed.ncbi.nlm.nih.gov/1685967/) - **thyrox_util_rate_T4U_reading**: the result of the test - **FTI_measured**: flag to indicate measurement on the Free Thyroxine Index (FTI)[https://endocrinology.testcatalog.org/show/FRTUP](https://endocrinology.testcatalog.org/show/FRTUP) - **FTI_reading**: the result of the test mentioned above - **ref_src**: [nominal] indicating the referral source of the patient ## Source Prepared and adatped by Gary Hutson hutsons-hacks@outlook.com , Dec-2021 and sourced from Garavan Institute and J. Ross Quinlan. ```r thyroid_disease ``` ## Examples ```r library(dplyr) library(ConfusionTableR) library(parsnip) library(rsample) library(recipes) library(ranger) library(workflows) data("thyroid_disease") td <- thyroid_disease # Create a factor of the class label to use in ML model td$ThryroidClass <- as.factor(td$ThryroidClass) # Check the structure of the data to make sure factor has been created str(td) # Remove missing values, or choose more advaced imputation option td <- td[complete.cases(td),] #Drop the column for referral source td <- td %>% dplyr::select(-ref_src) # Analyse class imbalance class_imbalance <- prop.table(table(td$ThryroidClass)) class_imbalance #Divide the data into a training test split set.seed(123) split <- rsample::initial_split(td, prop=3/4) train_data <- rsample::training(split) test_data <- rsample::testing(split) # Create recipe to upsample and normalise set.seed(123) td_recipe <- recipe(ThryroidClass ~ ., data=train_data) %>% step_normalize(all_predictors()) %>% step_zv(all_predictors()) # Instantiate the model set.seed(123) rf_mod <- parsnip::rand_forest() %>% set_engine("ranger") %>% set_mode("classification") # Create the model workflow td_wf <- workflow() %>% workflows::add_model(rf_mod) %>% workflows::add_recipe(td_recipe) # Fit the workflow to our training data set.seed(123) td_rf_fit <- td_wf %>% fit(data = train_data) # Extract the fitted data td_fitted <- td_rf_fit %>% extract_fit_parsnip() # Predict the test set on the training set to see model performance class_pred <- predict(td_rf_fit, test_data) td_preds <- test_data %>% bind_cols(class_pred) # Convert both to factors td_preds$.pred_class <- as.factor(td_preds$.pred_class) td_preds$ThryroidClass <- as.factor(td_preds$ThryroidClass) # Evaluate the data with ConfusionTableR cm <- ConfusionTableR::binary_class_cm(td_preds$ThryroidClass , td_preds$.pred_class, positive="sick") #View Confusion matrix cm$confusion_matrix #View record level cm$record_level_cm ``` ## References Thyroid disease records supplied by the Garavan Institute and J. Ross Quinlan.

thyroid_disease dataset