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Articles 1 - 2 of 2
Full-Text Articles in Statistical Models
Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth
Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth
Electronic Theses, Projects, and Dissertations
The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …
Ensemble Classification: An Analysis Of The Random Forest Model, Jarod Korn
Ensemble Classification: An Analysis Of The Random Forest Model, Jarod Korn
Williams Honors College, Honors Research Projects
The random forest model proposed by Dr. Leo Breiman in 2001 is an ensemble machine learning method for classification prediction and regression. In the following paper, we will conduct an analysis on the random forest model with a focus on how the model works, how it is applied in software, and how it performs on a set of data. To fully understand the model, we will introduce the concept of decision trees, give a summary of the CART model, explain in detail how the random forest model operates, discuss how the model is implemented in software, demonstrate the model by …