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Postoperative thyroglobulin as a yard-stick for radioiodine therapy: decision tree analysis in a European multicenter series of 1317 patients with differentiated thyroid cancer
PurposeAn accurate postoperative assessment is pivotal to inform postoperative 131 I treatment in patients with differentiated thyroid cancer (DTC)....
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A tree-based explainable AI model for early detection of Covid-19 using physiological data
With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing...
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Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach
BackgroundCOVID-19 pandemic emerged worldwide at the end of 2019, causing a severe global public health threat, and smoking is closely related to...
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Descriptive forest: experiments on a novel tree-structure-generalization method for describing cardiovascular diseases
BackgroundA decision tree is a crucial tool for describing the factors related to cardiovascular disease (CVD) risk and for predicting and explaining...
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Taekwondo win-loss determining factors using data mining-based decision tree analysis: focusing on game analysis for evidence-based coaching
BackgroundPurpose In this study, the purpose of this study is to identify the determinants of winning and losing in taekwondo by applying decision...
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Demographic Indicators of Probability Models
Abstract —Describing mortality dynamics using average indicators without considering variability can yield average results, impeding the analysis of...
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COVID-19 death risk predictors in Brazil using survival tree analysis: a retrospective cohort from 2020 to 2022
PurposeThis study analyses the survival of hospitalized patients with Severe Acute Respiratory Syndrome (SARS) due to COVID-19 and identifies the...
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Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges
BackgroundObjective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and...
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Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach
BackgroundAdvanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can...
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Identifying older inpatients at high risk of unintentional medication discrepancies: a classification tree analysis
Unintentional medication discrepancies at admission are differences between the best possible medication history and the prescribed treatment at...
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Identification of circulating miRNA as early diagnostic molecular markers in malignant glioblastoma base on decision tree joint scoring algorithm
PurposeThe lack of clinical markers prevents early diagnosis of glioblastoma (GBM). Many studies have found that circulating microRNAs (miRNAs) can...
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Hospital-based prostate cancer screening in vietnamese men with lower urinary tract symptoms: a classification and regression tree model
BackgroundProstate cancer (PCa) is a common disease in men over 65 years of age, and should be detected early, while reducing unnecessary biopsies....
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Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada
BackgroundPsoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on...
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Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System
BackgroundBiguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis...
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Cost-effectiveness of screening tools for identifying depression in early pregnancy: a decision tree model
BackgroundAlthough the effectiveness of screening tools for detecting depression in pregnancy has been investigated, there is limited evidence on the...
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Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model
PurposeGlioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual...
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Decision tree model predicts live birth after surgery for moderate-to-severe intrauterine adhesions
BackgroundAfter treatment of intrauterine adhesions, the rate of re-adhesion is high and the pregnancy outcome unpredictable and unsatisfactory. This...
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Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes
BackgroundWhen designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur...
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Conditional inference tree models to perceive depth of invasion in T1 colorectal cancer
Background and AimAccurate diagnosis of invasion depth for T1 colorectal cancer is of critical importance as it decides optimal resection technique....
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Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer’s Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator
Clinical trials are increasingly focused on pre-manifest and early Alzheimer’s disease (AD). Accurately predicting clinical progressions from normal...
