Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts
This study aims to investigate the relationship among alexithymia levels and automatic thoughts from cognitive behavioral therapy concepts. For this aim, Fisher Score analysis was applied to determine the most effective attributes of the automatic thoughts scale. In addition, the level of alexithymi...
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| Format: | Article |
| Language: | English |
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Psikiyatride Güncel Yaklaşımlar
2019-12-01
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| Series: | Psikiyatride Güncel Yaklaşımlar |
| Subjects: | |
| Online Access: | http://psikguncel.org/archives/vol11/no5/cap_11_05_06_en.pdf |
| _version_ | 1797328952455331840 |
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| author | Mustafa Kemal Yöntem Kemal Adem |
| author_facet | Mustafa Kemal Yöntem Kemal Adem |
| author_sort | Mustafa Kemal Yöntem |
| collection | DOAJ |
| description | This study aims to investigate the relationship among alexithymia levels and automatic thoughts from cognitive behavioral therapy concepts. For this aim, Fisher Score analysis was applied to determine the most effective attributes of the automatic thoughts scale. In addition, the level of alexithymia was predicted by the introduction of the data set into the machine learning methods of the Artificial Neural Network (ANN) and Support Vector Machine (SVM). It is aimed to develop a roadmap of what automatic thoughts should be given priorities in studies. The research, from 10 different provinces of Turkey, was performed with a total of 714 participants, of which 386 (54%) male and 328 (46%) female. Personal information form, Automatic Thoughts Scale and Toronto Alexithymia scale were applied to the participants. The data set obtained from the scale of automatic thoughts was applied to the feature selection by using the Fisher Score method and a data set containing 5 attributes was obtained. As a result of the implementation of the SVM method to this data set, the alexithymia level was predicted with 4.01 RMSE error. The results show that the features of the automatic thoughts are related to the alexithymia level. |
| first_indexed | 2024-03-08T06:59:20Z |
| format | Article |
| id | doaj.art-e80d9e3dcdf2446d8a0a2c94b638aec1 |
| institution | Directory Open Access Journal |
| issn | 1309-0674 |
| language | English |
| last_indexed | 2024-03-08T06:59:20Z |
| publishDate | 2019-12-01 |
| publisher | Psikiyatride Güncel Yaklaşımlar |
| record_format | Article |
| series | Psikiyatride Güncel Yaklaşımlar |
| spelling | doaj.art-e80d9e3dcdf2446d8a0a2c94b638aec12024-02-03T06:24:22ZengPsikiyatride Güncel YaklaşımlarPsikiyatride Güncel Yaklaşımlar1309-06742019-12-0111Supplement 1647810.18863/pgy.554788496Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic ThoughtsMustafa Kemal YöntemKemal AdemThis study aims to investigate the relationship among alexithymia levels and automatic thoughts from cognitive behavioral therapy concepts. For this aim, Fisher Score analysis was applied to determine the most effective attributes of the automatic thoughts scale. In addition, the level of alexithymia was predicted by the introduction of the data set into the machine learning methods of the Artificial Neural Network (ANN) and Support Vector Machine (SVM). It is aimed to develop a roadmap of what automatic thoughts should be given priorities in studies. The research, from 10 different provinces of Turkey, was performed with a total of 714 participants, of which 386 (54%) male and 328 (46%) female. Personal information form, Automatic Thoughts Scale and Toronto Alexithymia scale were applied to the participants. The data set obtained from the scale of automatic thoughts was applied to the feature selection by using the Fisher Score method and a data set containing 5 attributes was obtained. As a result of the implementation of the SVM method to this data set, the alexithymia level was predicted with 4.01 RMSE error. The results show that the features of the automatic thoughts are related to the alexithymia level.http://psikguncel.org/archives/vol11/no5/cap_11_05_06_en.pdfalexithymiaautomatic thoughtsmachine learning |
| spellingShingle | Mustafa Kemal Yöntem Kemal Adem Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts Psikiyatride Güncel Yaklaşımlar alexithymia automatic thoughts machine learning |
| title | Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts |
| title_full | Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts |
| title_fullStr | Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts |
| title_full_unstemmed | Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts |
| title_short | Prediction of the Level of Alexithymia through Machine Learning Methods Applied to Automatic Thoughts |
| title_sort | prediction of the level of alexithymia through machine learning methods applied to automatic thoughts |
| topic | alexithymia automatic thoughts machine learning |
| url | http://psikguncel.org/archives/vol11/no5/cap_11_05_06_en.pdf |
| work_keys_str_mv | AT mustafakemalyontem predictionofthelevelofalexithymiathroughmachinelearningmethodsappliedtoautomaticthoughts AT kemaladem predictionofthelevelofalexithymiathroughmachinelearningmethodsappliedtoautomaticthoughts |