Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms
Abstract Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acute PE population could significantly impro...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-00274-1 |
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| author | Mehmet Tahir Huyut Andrei Velichko Maksim Belyaev Yuriy Izotov Şebnem Karaoğlanoğlu Bünyamin Sertoğullarından Sıddık Keskin Dmitry Korzun |
| author_facet | Mehmet Tahir Huyut Andrei Velichko Maksim Belyaev Yuriy Izotov Şebnem Karaoğlanoğlu Bünyamin Sertoğullarından Sıddık Keskin Dmitry Korzun |
| author_sort | Mehmet Tahir Huyut |
| collection | DOAJ |
| description | Abstract Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acute PE population could significantly improve diagnosis and treatment, potentially reducing mortality rates. This study evaluates the performance of LogNNet and supervised machine learning (ML) models for diagnosing RVD using a repeated stratified hold-out validation procedure. An ensemble-based LogNNet model is proposed for practical application. The LogNNet model identified gender, coronary artery disease, Comorbid Disease (especially hypertension), age (above 74-years), Thrombus segment and un/bilateral Thrombus as the most significant predictors for RVD diagnosis. Additionally, combinations of these features demonstrated high predictive power. LogNNet achieved robust results with only a few selected features, making it suitable for applications in resource-limited environments. LogNNet provides a practical and accessible tool for early RVD detection using PE patient data and has been shown to support applications in healthcare innovations aimed at improving patient outcomes and resilience in edge devices, clinical decision support systems, and challenging environments. Furthermore, these findings could be used as promising applications by integrating with advances in digital health and human health monitoring systems, such as bionic clothing and smart sensor networks. |
| format | Article |
| id | doaj-art-a44e2a5b8b6e4eba839d19a88175e8d1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a44e2a5b8b6e4eba839d19a88175e8d12025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-00274-1Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithmsMehmet Tahir Huyut0Andrei Velichko1Maksim Belyaev2Yuriy Izotov3Şebnem Karaoğlanoğlu4Bünyamin Sertoğullarından5Sıddık Keskin6Dmitry Korzun7Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım UniversityPetrozavodsk State UniversityPetrozavodsk State UniversityPetrozavodsk State UniversityDepartment of Pulmonary Medicine, Faculty of Medicine, İzmir Katip Çelebi UniversityDepartment of Pulmonary Medicine, Faculty of Medicine, İzmir Katip Çelebi UniversityDepartment of Biostatistics, Faculty of Medicine, Van Yuzuncu Yıl UniversityPetrozavodsk State UniversityAbstract Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acute PE population could significantly improve diagnosis and treatment, potentially reducing mortality rates. This study evaluates the performance of LogNNet and supervised machine learning (ML) models for diagnosing RVD using a repeated stratified hold-out validation procedure. An ensemble-based LogNNet model is proposed for practical application. The LogNNet model identified gender, coronary artery disease, Comorbid Disease (especially hypertension), age (above 74-years), Thrombus segment and un/bilateral Thrombus as the most significant predictors for RVD diagnosis. Additionally, combinations of these features demonstrated high predictive power. LogNNet achieved robust results with only a few selected features, making it suitable for applications in resource-limited environments. LogNNet provides a practical and accessible tool for early RVD detection using PE patient data and has been shown to support applications in healthcare innovations aimed at improving patient outcomes and resilience in edge devices, clinical decision support systems, and challenging environments. Furthermore, these findings could be used as promising applications by integrating with advances in digital health and human health monitoring systems, such as bionic clothing and smart sensor networks.https://doi.org/10.1038/s41598-025-00274-1Right ventricular dysfunctionPulmonary embolismThrombosisLogNNetMachine learningDiagnostic models |
| spellingShingle | Mehmet Tahir Huyut Andrei Velichko Maksim Belyaev Yuriy Izotov Şebnem Karaoğlanoğlu Bünyamin Sertoğullarından Sıddık Keskin Dmitry Korzun Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms Scientific Reports Right ventricular dysfunction Pulmonary embolism Thrombosis LogNNet Machine learning Diagnostic models |
| title | Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms |
| title_full | Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms |
| title_fullStr | Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms |
| title_full_unstemmed | Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms |
| title_short | Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms |
| title_sort | identification of right ventricular dysfunction with lognnet based diagnostic model a comparative study with supervised ml algorithms |
| topic | Right ventricular dysfunction Pulmonary embolism Thrombosis LogNNet Machine learning Diagnostic models |
| url | https://doi.org/10.1038/s41598-025-00274-1 |
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