,
Asif Laldin [aut],
Isabella Velásquez [aut]| Title: | Collection of Machine Learning Datasets for Supervised Machine Learning |
|---|---|
| Description: | Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains. |
| Authors: | Gary Hutson [aut, cre] ,
Asif Laldin [aut],
Isabella Velásquez [aut] |
| Maintainer: | Gary Hutson <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.1 |
| Built: | 2025-01-16 03:53:46 UTC |
| Source: | https://github.com/statsgary/mldatar |
a NHS patient safety incidents dataset: https://www.england.nhs.uk/patient-safety/report-patient-safety-incident/ dataset that has been synthetically generated against real data
care_home_incidentscare_home_incidents
A data frame with 1216 rows and 12 variables:
a binary indicator to specify whether a certain care home is failing
aggregation of incidents indicating weight loss in patient
medication missed aggregaation
Recorded number of patient falls
Number of patient choking incidents
unexpected deaths in the care home
Number of bruising incidents in the care home
Absconding from the care home setting
Abuse conducted by one care home resident against another
Incidents of resident abuse by staff
Incidents of residents abusing staff
Unexplained wounds against staff
Collected by Gary Hutson [email protected], Jan-2022
library(dplyr) data(care_home_incidents) # Convert diabetes data to factor' ch_incs <- care_home_incidents %>% mutate(CareHomeFail = as.factor(CareHomeFail)) ch_incs %>% glimpse() # Check factor factor(ch_incs$CareHomeFail)library(dplyr) data(care_home_incidents) # Convert diabetes data to factor' ch_incs <- care_home_incidents %>% mutate(CareHomeFail = as.factor(CareHomeFail)) ch_incs %>% glimpse() # Check factor factor(ch_incs$CareHomeFail)
csgo
csgocsgo
A data frame with 1,133 rows and 17 variables:
Map on which the match was played
Day of the month
Month of the year
Year
Date of match DD/MM/YYYY
Time waited to find match
Total match length in seconds
Number of rounds played as Team A
Number of rounds played as Team B
Maximum ping in milliseconds;the signal that's sent from one computer to another on the same network
Number of kills accumulated in match; max 5 per round
Number of assists accumulated in a match,inflicting oppononent with more than 50 percent damage,who is then killed by another player accumulated in match max 5 per round
Number of times player died during match;max 1 per round
Most Valuable Player award
Percentage of kills that were a result from a shot to opponent's head
Number of points accumulated during match. Apoints are gained from kills, assists,bomb defuses & bomb plants. Points are lost for sucicide and friendly kills
The result of the match, Win, Loss, Draw
Extracted by Asif Laldin [email protected], March-2019
Diabetes datasets
diabetes_datadiabetes_data
A data frame with 520 rows and 17 variables:
age of the patient presenting with diabetes
gender of the patient with diabetes
if the patient has a history of excessive urination
abnormal thurst, accompanied by the excessive intake of water or fluid
Sudden weight loss that has recently occured
Fatigue or weakness
excessive or extreme hunger
patient has thrush fungus on or near their genital region
history of blurred vision
skin itching
general irritability and mood issues
delayed healing of wounds
partial psoriasis on the body
stiffness of the muscles
scalp alopecia and hair shedding
Classified as obese
Class label to indicate whether the patient is diabetic or not
Collected by Gary Hutson [email protected], Dec-2021
library(dplyr) data(diabetes_data) # Convert diabetes data to factor' diabetes_data <- diabetes_data %>% glimpse() %>% mutate(DiabeticClass = as.factor(DiabeticClass)) is.factor(diabetes_data$DiabeticClass)library(dplyr) data(diabetes_data) # Convert diabetes data to factor' diabetes_data <- diabetes_data %>% glimpse() %>% mutate(DiabeticClass = as.factor(DiabeticClass)) is.factor(diabetes_data$DiabeticClass)
The dataset is to be used with a supervised classification ML model to classify heart disease.
heartdiseaseheartdisease
A data frame with 918 rows and 10 variables:
age of the patient presenting with heart disease
gender of the patient
blood pressure for resting heart beat
Cholesterol reading
blood sample of glucose after a patient fasts https://www.diabetes.co.uk/diabetes_care/fasting-blood-sugar-levels.html
Resting echocardiography is an indicator of previous myocardial infarction e.g. heart attack
Maximum heart rate
chest pain caused by decreased flood flow https://www.nhs.uk/conditions/angina/
reading at the peak of the heart rate
the classification label of whether patient has heart disease or not
Collected by Gary Hutson [email protected], Dec-2021
library(dplyr) library(ConfusionTableR) data(heartdisease) # Convert diabetes data to factor' hd <- heartdisease %>% glimpse() %>% mutate(HeartDisease = as.factor(HeartDisease)) # Check that the label is now a factor is.factor(hd$HeartDisease) # Dummy encoding # Get categorical columns hd_cat <- hd %>% dplyr::select_if(is.character) # Dummy encode the categorical variables # Specify the columns to encode cols <- c("RestingECG", "Angina", "Sex") # Dummy encode using dummy_encoder in ConfusionTableR package coded <- ConfusionTableR::dummy_encoder(hd_cat, cols, remove_original = TRUE) coded <- coded %>% select(RestingECG_ST, RestingECG_LVH, Angina=Angina_Y, Sex=Sex_F) # Remove column names we have encoded from original data frame hd_one <- hd[,!names(hd) %in% cols] # Bind the numerical data on to the categorical data hd_final <- bind_cols(coded, hd_one) # Output the final encoded data frame for the ML task glimpse(hd_final)library(dplyr) library(ConfusionTableR) data(heartdisease) # Convert diabetes data to factor' hd <- heartdisease %>% glimpse() %>% mutate(HeartDisease = as.factor(HeartDisease)) # Check that the label is now a factor is.factor(hd$HeartDisease) # Dummy encoding # Get categorical columns hd_cat <- hd %>% dplyr::select_if(is.character) # Dummy encode the categorical variables # Specify the columns to encode cols <- c("RestingECG", "Angina", "Sex") # Dummy encode using dummy_encoder in ConfusionTableR package coded <- ConfusionTableR::dummy_encoder(hd_cat, cols, remove_original = TRUE) coded <- coded %>% select(RestingECG_ST, RestingECG_LVH, Angina=Angina_Y, Sex=Sex_F) # Remove column names we have encoded from original data frame hd_one <- hd[,!names(hd) %in% cols] # Bind the numerical data on to the categorical data hd_final <- bind_cols(coded, hd_one) # Output the final encoded data frame for the ML task glimpse(hd_final)
classification dataset of long staying patients. Contains patients who have been registered as an inpatient for longer than 7 days length of stay https://www.england.nhs.uk/south/wp-content/uploads/sites/6/2016/12/rig-reviewing-stranded-patients-hospital.pdf.
long_stayerslong_stayers
A data frame with 768 rows and 9 variables:
binary classification label indicating whether stranded = 1 or not stranded=0
age of the patient
flag indicating whether referred from a private care home - 1=Care Home Referral and 0=Not a care home referral
flag indicating whether they are medically safe for discharge - 1=Medically safe and 0=Not medically safe
flag indicating health care for older person triage - 1=Yes triaged from HCOP and 0=Triaged from different department
flag indicating whether they require mental health care - 1=MH assistance needed and 0=No history of mental health
Count of the number of times they have been in hospital in last 12 months
date the patient was admitted as an inpatient
indicates the type of frailty - nominal variable
Prepared, acquired and adatped by Gary Hutson [email protected], Dec-2021. Synthetic data, based off live patient data from various NHS secondary health care trusts.
library(dplyr) library(ggplot2) library(caret) library(rsample) library(varhandle) data("long_stayers") glimpse(long_stayers) # Examine class imbalance prop.table(table(long_stayers$stranded.label)) # Feature engineering long_stayers <- long_stayers %>% dplyr::mutate(stranded.label=factor(stranded.label)) %>% dplyr::select(everything(), -c(admit_date)) # Feature encoding cats <- select_if(long_stayers, is.character) cat_dummy <- varhandle::to.dummy(cats$frailty_index, "frail_ind") #Converts the frailty index column to dummy encoding and sets a column called "frail_ind" prefix cat_dummy <- cat_dummy %>% as.data.frame() %>% dplyr::select(-frail_ind.No_index_item) #Drop the field of interest long_stayers <- long_stayers %>% dplyr::select(-frailty_index) %>% bind_cols(cat_dummy) %>% na.omit(.) # Split the data split <- rsample::initial_split(long_stayers, prop = 3/4) train <- rsample::training(split) test <- rsample::testing(split) set.seed(123) glm_class_mod <- caret::train(factor(stranded.label) ~ ., data = train, method = "glm") print(glm_class_mod) # Predict the probabilities preds <- predict(glm_class_mod, newdata = test) # Predict class pred_prob <- predict(glm_class_mod, newdata = test, type="prob") #Predict probs predicted <- data.frame(preds, pred_prob) test <- test %>% bind_cols(predicted) %>% dplyr::rename(pred_class=preds) #Evaluate with ConfusionTableR library(ConfusionTableR) cm <- ConfusionTableR::binary_class_cm(test$stranded.label, test$pred_class, positive="Stranded") cm$record_level_cm # Visualise odds ration library(OddsPlotty) plotty <- OddsPlotty::odds_plot(glm_class_mod$finalModel, title = "Odds Plot ", subtitle = "Showing odds of patient stranded", point_col = "#00f2ff", error_bar_colour = "black", point_size = .5, error_bar_width = .8, h_line_color = "red") print(plotty)library(dplyr) library(ggplot2) library(caret) library(rsample) library(varhandle) data("long_stayers") glimpse(long_stayers) # Examine class imbalance prop.table(table(long_stayers$stranded.label)) # Feature engineering long_stayers <- long_stayers %>% dplyr::mutate(stranded.label=factor(stranded.label)) %>% dplyr::select(everything(), -c(admit_date)) # Feature encoding cats <- select_if(long_stayers, is.character) cat_dummy <- varhandle::to.dummy(cats$frailty_index, "frail_ind") #Converts the frailty index column to dummy encoding and sets a column called "frail_ind" prefix cat_dummy <- cat_dummy %>% as.data.frame() %>% dplyr::select(-frail_ind.No_index_item) #Drop the field of interest long_stayers <- long_stayers %>% dplyr::select(-frailty_index) %>% bind_cols(cat_dummy) %>% na.omit(.) # Split the data split <- rsample::initial_split(long_stayers, prop = 3/4) train <- rsample::training(split) test <- rsample::testing(split) set.seed(123) glm_class_mod <- caret::train(factor(stranded.label) ~ ., data = train, method = "glm") print(glm_class_mod) # Predict the probabilities preds <- predict(glm_class_mod, newdata = test) # Predict class pred_prob <- predict(glm_class_mod, newdata = test, type="prob") #Predict probs predicted <- data.frame(preds, pred_prob) test <- test %>% bind_cols(predicted) %>% dplyr::rename(pred_class=preds) #Evaluate with ConfusionTableR library(ConfusionTableR) cm <- ConfusionTableR::binary_class_cm(test$stranded.label, test$pred_class, positive="Stranded") cm$record_level_cm # Visualise odds ration library(OddsPlotty) plotty <- OddsPlotty::odds_plot(glm_class_mod$finalModel, title = "Odds Plot ", subtitle = "Showing odds of patient stranded", point_col = "#00f2ff", error_bar_colour = "black", point_size = .5, error_bar_width = .8, h_line_color = "red") print(plotty)
PreDiabetes dataset
PreDiabetesPreDiabetes
A data frame with 3059 rows and 9 variables:
age of the patient presenting with diabetes
sex of the patient with diabetes
Index of Multiple Deprivation Decile
Body Mass Index of patient
age at pre diabetes diagnosis
average blood glucose mmol/mol
time in years between pre-diabetes and diabetes diagnosis
age at diabetes diagnosis
number of pre-diabetes related primary care appointments before diabetes diagnosis
Generated by Asif Laldin [email protected], Jan-2022
library(dplyr) data(PreDiabetes) # Convert diabetes data to factor' diabetes_data <- PreDiabetes %>% glimpse()library(dplyr) data(PreDiabetes) # Convert diabetes data to factor' diabetes_data <- PreDiabetes %>% glimpse()
This dataset has been obtained from a Stoke department within the NHS and is a traditional supervised ML classification dataset
stroke_classificationstroke_classification
A data frame with 5110 rows and 11 variables:
unique patient identifier index
outcome variable as a flag - 1 for stroke and 0 for no stroke
patient gender description
age of the patient
binary flag to indicate whether patient has hypertension: https://www.nhs.uk/conditions/high-blood-pressure-hypertension/
binary flag to indicate whether patient has heart disease: 1 or no heart disease history: 0
binary flag to indicate whether patient has history of work related stress
binary flag indicating whether patient lives in an urban area or not
average blood glucose readings of the patient
body mass index of the patient: https://www.nhs.uk/live-well/healthy-weight/bmi-calculator/
binary flag to indicate if the patient smokes - 1 for current smoker and 0 for smoking cessation
Prepared and compiled by Gary Hutson [email protected], Apr-2022.
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.
thyroid_diseasethyroid_disease
A data frame with 3772 rows and 28 variables:
binary classification label indicating whether sick = 1 or negative=0
age of the patient
flag indicating gender of patient - 1=Female and 0=Male
flag to indicate whether thyroxine replacement prescribed 1=Thyroxine prescribed
flag to indicate query has been actioned
flag to indicate whether anti-thyroid medicine has been prescribed
flag to indicate sickness due to thyroxine depletion or over activity
flag to indicate whether the patient is pregnant
flag to indicate whether the patient has had thyroid surgery
indicates whether patient has had radioactive iodine treatment: https://www.nhs.uk/conditions/thyroid-cancer/treatment/
flag to indicate under active thyroid query https://www.nhs.uk/conditions/underactive-thyroid-hypothyroidism/
flag to indicate over active thyroid query https://www.nhs.uk/conditions/overactive-thyroid-hyperthyroidism/
Lithium carbonate administered to decrease the level of thyroid hormones
flag to indicate swelling of the thyroid gland https://www.nhs.uk/conditions/goitre/
flag to indicate a tumor
flag to indicate a diagnosed under active thyroid
indicates whether a patient has a psychological condition
a TSH level lower than normal indicates there is usually more than enough thyroid hormone in the body and may indicate hyperthyroidism
the reading result of the TSH blood test
linked to TSH reading - when free triiodothyronine rise above normal this indicates hyperthyroidism
the reading result of the T3 blood test looking for above normal levels of free triiodothyronine
free thyroxine, also known as T4, is used with T3 and TSH tests to diagnose hyperthyroidism
the reading result of th T4 test
flag indicating the thyroxine utilisation rate https://pubmed.ncbi.nlm.nih.gov/1685967/
the result of the test
flag to indicate measurement on the Free Thyroxine Index (FTI)https://endocrinology.testcatalog.org/show/FRTUP
the result of the test mentioned above
[nominal] indicating the referral source of the patient
Prepared and adatped by Gary Hutson [email protected], Dec-2021 and sourced from Garavan Institute and J. Ross Quinlan.
Thyroid disease records supplied by the Garavan Institute and J. Ross Quinlan.
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$record_level_cmlibrary(dplyr) library(ConfusionTableR) library(parsnip) library(rsample) library(recipes) library(ranger) library(workflows) data("thyroid_disease") td <- thyroid_disease # Create a factor of the class label to use in ML model td$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