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Full-Text Articles in Physical Sciences and Mathematics
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 …
Distance Correlation Based Feature Selection In Random Forest, Jose Munoz-Lopez
Distance Correlation Based Feature Selection In Random Forest, Jose Munoz-Lopez
Electronic Theses, Projects, and Dissertations
The Pearson correlation coefficient is a commonly used measure of correlation, but it has limitations as it only measures the linear relationship between two numerical variables. In 2007, Szekely et al. introduced the distance correlation, which measures all types of dependencies between random vectors X and Y in arbitrary dimensions, not just the linear ones. In this thesis, we propose a filter method that utilizes distance correlation as a criterion for feature selection in Random Forest regression. We conduct extensive simulation studies to evaluate its performance compared to existing methods under various data settings, in terms of the prediction mean …