| thyroid_disease {MLDataR} | R Documentation |
Thyroid disease dataset
Description
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.
Usage
thyroid_disease
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/
- query_hypothyroid
flag to indicate under active thyroid query 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/
- 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/
- 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/
- 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
- 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.
References
Thyroid disease records supplied by the Garavan Institute and J. Ross Quinlan.
Examples
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