Sökning: WFRF:(Berglund Johan Sanmartin)
> (2024) >
Optimizing Depressi...
Optimizing Depression Prediction in Older Adults : A Comparative Study of Feature Extraction and Machine Learning Models
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- Javeed, Ashir, 1989- (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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- Anderberg, Peter, Professor, 1963- (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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- Ghazi, Ahmad Nauman, 1983- (författare)
- Blekinge Tekniska Högskola,Institutionen för programvaruteknik
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visa fler...
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Javeed, Asim (författare)
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- Moraes, Ana Luiza Dallora (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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- Sanmartin Berglund, Johan, Professor (författare)
- Blekinge Tekniska Högskola,Institutionen för hälsa
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visa färre...
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K- nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- feature extraction
- machine learning
- depression
- classification
- Tillämpad hälsoteknik
- Applied Health Technology
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)