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Aug 7, 2024 · In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent ...
According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As ...
Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and. Machine Learning Models. 1st Ashir Javeed. 2nd Peter Anderberg.
Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and Machine Learning Models. Ashir Javeed, Peter Anderberg, Ahmad ...
Optimizing Depression Prediction in Older Adults : A Comparative Study of Feature Extraction and Machine Learning Models. Javeed, Ashir, 1989- (author) ...
Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and Machine Learning Models. Conference Paper. Full-text available.
Nov 27, 2023 · This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression.
Missing: Optimizing | Show results with:Optimizing
We estimate the risk of depression in old age by combining adult life course trajectories and childhood conditions in supervised machine learning algorithms.
Mar 10, 2024 · This research investigates the reliability of five prominent ML algorithms—a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, ...
Jun 27, 2024 · Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics.
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