Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health
The presented study explores the clustering of arterial oscillogram (AO) data among a sample of patients, focusing on ultra-low-frequency (ULF) indicators and their relationship with depression levels. Through dimensionality reduction using UMAP, two distinct classes emerged, categorized as lighter...
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| Format: | Article |
| Language: | English |
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Riga Technical University Press
2024-10-01
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| Series: | Complex Systems Informatics and Modeling Quarterly |
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| Online Access: | https://csimq-journals.rtu.lv/article/view/8982 |
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| author | Vladislav Kaverinsky Dmytro Vakulenko Liudmyla Vakulenko Kyrylo Malakhov |
| author_facet | Vladislav Kaverinsky Dmytro Vakulenko Liudmyla Vakulenko Kyrylo Malakhov |
| author_sort | Vladislav Kaverinsky |
| collection | DOAJ |
| description | The presented study explores the clustering of arterial oscillogram (AO) data among a sample of patients, focusing on ultra-low-frequency (ULF) indicators and their relationship with depression levels. Through dimensionality reduction using UMAP, two distinct classes emerged, categorized as lighter and more severe cases. Utilizing machine learning methods, an automated classifier was developed based on correlated ULF indicators, which led to improved classification accuracy. By incorporating ULF parameters, products of correlated parameters, and additional measured factors, the classifier achieved high reliability in estimating depression levels. Specifically, the nearest neighbors method yielded accuracies up to 0.9792. This research supports the creation of an automated diagnostic classification AI service capable of reliably estimating at least four levels of depression based on AO analysis. |
| format | Article |
| id | doaj-art-6d5ed4e7dc6f47508e07b55eb1a4be48 |
| institution | Kabale University |
| issn | 2255-9922 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | Complex Systems Informatics and Modeling Quarterly |
| spelling | doaj-art-6d5ed4e7dc6f47508e07b55eb1a4be482025-02-03T12:03:20ZengRiga Technical University PressComplex Systems Informatics and Modeling Quarterly2255-99222024-10-010409411010.7250/csimq.2024-40.043741Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular HealthVladislav Kaverinsky0Dmytro Vakulenko1Liudmyla Vakulenko2Kyrylo Malakhov3Frantsevic Institute for Problems in Material Science of the National Academy of Sciences of Ukraine, Kyiv, UkraineMedical Informatics Department, Horbachevsky Ternopil National Medical University, Ternopil, UkraineTernopil Volodymyr Hnatiuk National Pedagogical University, Ternopil, UkraineMicroprocessor technology lab, Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, Kyiv, UkraineThe presented study explores the clustering of arterial oscillogram (AO) data among a sample of patients, focusing on ultra-low-frequency (ULF) indicators and their relationship with depression levels. Through dimensionality reduction using UMAP, two distinct classes emerged, categorized as lighter and more severe cases. Utilizing machine learning methods, an automated classifier was developed based on correlated ULF indicators, which led to improved classification accuracy. By incorporating ULF parameters, products of correlated parameters, and additional measured factors, the classifier achieved high reliability in estimating depression levels. Specifically, the nearest neighbors method yielded accuracies up to 0.9792. This research supports the creation of an automated diagnostic classification AI service capable of reliably estimating at least four levels of depression based on AO analysis.https://csimq-journals.rtu.lv/article/view/8982machine learningtransdisciplinary researchdata clusteringumaparterial oscillogramulfmental state diagnostic |
| spellingShingle | Vladislav Kaverinsky Dmytro Vakulenko Liudmyla Vakulenko Kyrylo Malakhov Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health Complex Systems Informatics and Modeling Quarterly machine learning transdisciplinary research data clustering umap arterial oscillogram ulf mental state diagnostic |
| title | Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health |
| title_full | Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health |
| title_fullStr | Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health |
| title_full_unstemmed | Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health |
| title_short | Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health |
| title_sort | machine learning analysis of arterial oscillograms for depression level diagnosis in cardiovascular health |
| topic | machine learning transdisciplinary research data clustering umap arterial oscillogram ulf mental state diagnostic |
| url | https://csimq-journals.rtu.lv/article/view/8982 |
| work_keys_str_mv | AT vladislavkaverinsky machinelearninganalysisofarterialoscillogramsfordepressionleveldiagnosisincardiovascularhealth AT dmytrovakulenko machinelearninganalysisofarterialoscillogramsfordepressionleveldiagnosisincardiovascularhealth AT liudmylavakulenko machinelearninganalysisofarterialoscillogramsfordepressionleveldiagnosisincardiovascularhealth AT kyrylomalakhov machinelearninganalysisofarterialoscillogramsfordepressionleveldiagnosisincardiovascularhealth |