tree: Medical Decision Tree

In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

Medical Decision Tree

Usage

tree(
  data,
  vars,
  facs,
  target,
  targetLevel,
  train,
  trainLevel,
  imputeMissing = FALSE,
  balanceClasses = FALSE,
  scaleFeatures = FALSE,
  clinicalMetrics = FALSE,
  featureImportance = FALSE,
  showInterpretation = FALSE,
  showPlot = FALSE,
  minCases = 10,
  maxDepth = 4,
  confidenceInterval = FALSE,
  riskStratification = FALSE,
  exportPredictions = FALSE,
  clinicalContext = "diagnosis",
  costRatio = 1,
  prevalenceAdjustment = FALSE,
  expectedPrevalence = 10
)

Arguments

data	The data as a data frame containing clinical variables, biomarkers, and patient outcomes.
vars	Continuous variables such as biomarker levels, age, laboratory values, or quantitative pathological measurements.
facs	Categorical variables such as tumor grade, stage, histological type, or patient demographics.
target	Primary outcome variable: disease status, treatment response, survival status, or diagnostic category.
targetLevel	Level representing disease presence, positive outcome, or event of interest.
train	Variable indicating training vs validation cohorts. If not provided, data will be split automatically.
trainLevel	Level indicating the training/discovery cohort.
imputeMissing	Impute missing values using medically appropriate methods (median within disease groups for continuous, mode for categorical).
balanceClasses	Balance classes to handle rare diseases or imbalanced outcomes. Recommended for disease prevalence <20\ \item scaleFeaturesStandardize continuous variables (useful when combining biomarkers with different scales/units). \item clinicalMetricsDisplay sensitivity, specificity, predictive values, likelihood ratios, and other clinical metrics. \item featureImportanceIdentify most important clinical variables and biomarkers for the decision tree. \item showInterpretationProvide clinical interpretation of results including diagnostic utility and clinical recommendations. \item showPlotDisplay visual representation of the decision tree. \item minCasesMinimum number of cases required in each terminal node (higher values prevent overfitting). \item maxDepthMaximum depth of decision tree (deeper trees may overfit). \item confidenceIntervalDisplay confidence intervals for performance metrics. \item riskStratificationAnalyze risk stratification performance and create risk categories based on tree predictions. \item exportPredictionsAdd predicted classifications and probabilities to the dataset. \item clinicalContextClinical context affects interpretation thresholds and recommendations (e.g., screening requires high sensitivity). \item costRatioRelative cost of missing a case vs false alarm. Higher values favor sensitivity over specificity. \item prevalenceAdjustmentAdjust predictive values for expected disease prevalence in target population (different from study sample). \item expectedPrevalenceExpected disease prevalence in target population for adjusted predictive value calculations.

A results object containing:

results$todo					a html
results$text1					a preformatted
results$text2					a preformatted
results$text2a					a preformatted
results$text2b					a preformatted
results$text3					a preformatted
results$text4					a html
results$dataQuality					a preformatted
results$missingDataReport					a table
results$modelSummary					a html
results$clinicalMetrics					a table
results$clinicalInterpretation					a html
results$featureImportance					a table
results$riskStratification					a table
results$confusionMatrix					a table
results$adjustedMetrics					a table
results$plot					an image
results$deploymentGuidelines					a html
results$predictions					an output
results$probabilities					an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$missingDataReport$asDF

as.data.frame(results$missingDataReport) Enhanced decision tree analysis for medical research, pathology and oncology. Provides clinical performance metrics, handles missing data appropriately, and offers interpretations relevant to medical decision-making. # Example for cancer diagnosis data(cancer_biomarkers) tree( data = cancer_biomarkers, vars = c("PSA", "age", "tumor_size"), facs = c("grade", "stage"), target = "diagnosis", targetLevel = "cancer", train = "cohort", trainLevel = "discovery", imputeMissing = TRUE, balanceClasses = TRUE )

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.