Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method
This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/8/4/392 |
| _version_ | 1797551432831860736 |
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| author | Nizar Ahmed Fatih Dilmaç Adil Alpkocak |
| author_facet | Nizar Ahmed Fatih Dilmaç Adil Alpkocak |
| author_sort | Nizar Ahmed |
| collection | DOAJ |
| description | This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification. |
| first_indexed | 2024-03-10T15:44:38Z |
| format | Article |
| id | doaj.art-b0cc4560974445739bf52492929c1753 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T15:44:38Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-b0cc4560974445739bf52492929c17532023-11-20T16:32:29ZengMDPI AGHealthcare2227-90322020-10-018439210.3390/healthcare8040392Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation MethodNizar Ahmed0Fatih Dilmaç1Adil Alpkocak2Department of Computer Engineering, Dokuz Eylul University, Tinaztepe Kampusu, 35160 Izmir, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Tinaztepe Kampusu, 35160 Izmir, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Tinaztepe Kampusu, 35160 Izmir, TurkeyThis study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.https://www.mdpi.com/2227-9032/8/4/392biomedical text classificationmulticlass classificationcardiovascular diseasesdeep neural networkfeature representationbidirectional long short-term memory |
| spellingShingle | Nizar Ahmed Fatih Dilmaç Adil Alpkocak Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method Healthcare biomedical text classification multiclass classification cardiovascular diseases deep neural network feature representation bidirectional long short-term memory |
| title | Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method |
| title_full | Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method |
| title_fullStr | Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method |
| title_full_unstemmed | Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method |
| title_short | Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method |
| title_sort | classification of biomedical texts for cardiovascular diseases with deep neural network using a weighted feature representation method |
| topic | biomedical text classification multiclass classification cardiovascular diseases deep neural network feature representation bidirectional long short-term memory |
| url | https://www.mdpi.com/2227-9032/8/4/392 |
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