ML algorithms used in this study.
<div><p>Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present an avenue for further investi...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852020393757376512 |
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| author | Toheeb Salahudeen (21368040) |
| author2 | Maher Maalouf (6318215) Ibrahim (Abe) M. Elfadel (21368043) Herbert F. Jelinek (7039787) |
| author2_role | author author author |
| author_facet | Toheeb Salahudeen (21368040) Maher Maalouf (6318215) Ibrahim (Abe) M. Elfadel (21368043) Herbert F. Jelinek (7039787) |
| author_role | author |
| dc.creator.none.fl_str_mv | Toheeb Salahudeen (21368040) Maher Maalouf (6318215) Ibrahim (Abe) M. Elfadel (21368043) Herbert F. Jelinek (7039787) |
| dc.date.none.fl_str_mv | 2025-05-15T14:57:23Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320955.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/ML_algorithms_used_in_this_study_/29075070 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified machine learning present machine learning models global mental health 56 %, respectively 26 %, respectively 1 %, respectively unbalanced data sets aid clinical assessment multiclass classification scenarios including random forest random forest clinical indicators three classes significant challenge shedding light rf achieved recent advances precise relationship often intertwined mitochondrial peptides five classes findings underscore f1 score diabetes mellitus comorbid conditions balanced data |
| dc.title.none.fl_str_mv | ML algorithms used in this study. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present an avenue for further investigation. This study employed advanced machine learning techniques to classify major depressive disorders based on clinical indicators and mitochondrial oxidative stress markers. Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. Results indicate promising accuracy and precision, particularly with Random Forest on balanced data. RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. Including only oxidative stress markers resulted in accuracy and an F1 score of 79.52% and 80.56%, respectively. Notably, including mitochondrial peptides alongside clinical factors significantly enhances predictive capability, shedding light on the interplay between depression severity and mitochondrial oxidative stress pathways. These findings underscore the potential for machine learning models to aid clinical assessment, particularly in individuals with comorbid conditions such as hypertension, diabetes mellitus, and cardiovascular disease.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_7df6f9a4e654724724182982cdcd0892 |
| identifier_str_mv | 10.1371/journal.pone.0320955.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29075070 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ML algorithms used in this study.Toheeb Salahudeen (21368040)Maher Maalouf (6318215)Ibrahim (Abe) M. Elfadel (21368043)Herbert F. Jelinek (7039787)Cell BiologyBiotechnologyScience PolicyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedmachine learning presentmachine learning modelsglobal mental health56 %, respectively26 %, respectively1 %, respectivelyunbalanced data setsaid clinical assessmentmulticlass classification scenariosincluding random forestrandom forestclinical indicatorsthree classessignificant challengeshedding lightrf achievedrecent advancesprecise relationshipoften intertwinedmitochondrial peptidesfive classesfindings underscoref1 scorediabetes mellituscomorbid conditionsbalanced data<div><p>Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present an avenue for further investigation. This study employed advanced machine learning techniques to classify major depressive disorders based on clinical indicators and mitochondrial oxidative stress markers. Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. Results indicate promising accuracy and precision, particularly with Random Forest on balanced data. RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. Including only oxidative stress markers resulted in accuracy and an F1 score of 79.52% and 80.56%, respectively. Notably, including mitochondrial peptides alongside clinical factors significantly enhances predictive capability, shedding light on the interplay between depression severity and mitochondrial oxidative stress pathways. These findings underscore the potential for machine learning models to aid clinical assessment, particularly in individuals with comorbid conditions such as hypertension, diabetes mellitus, and cardiovascular disease.</p></div>2025-05-15T14:57:23ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0320955.t005https://figshare.com/articles/dataset/ML_algorithms_used_in_this_study_/29075070CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290750702025-05-15T14:57:23Z |
| spellingShingle | ML algorithms used in this study. Toheeb Salahudeen (21368040) Cell Biology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified machine learning present machine learning models global mental health 56 %, respectively 26 %, respectively 1 %, respectively unbalanced data sets aid clinical assessment multiclass classification scenarios including random forest random forest clinical indicators three classes significant challenge shedding light rf achieved recent advances precise relationship often intertwined mitochondrial peptides five classes findings underscore f1 score diabetes mellitus comorbid conditions balanced data |
| status_str | publishedVersion |
| title | ML algorithms used in this study. |
| title_full | ML algorithms used in this study. |
| title_fullStr | ML algorithms used in this study. |
| title_full_unstemmed | ML algorithms used in this study. |
| title_short | ML algorithms used in this study. |
| title_sort | ML algorithms used in this study. |
| topic | Cell Biology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified machine learning present machine learning models global mental health 56 %, respectively 26 %, respectively 1 %, respectively unbalanced data sets aid clinical assessment multiclass classification scenarios including random forest random forest clinical indicators three classes significant challenge shedding light rf achieved recent advances precise relationship often intertwined mitochondrial peptides five classes findings underscore f1 score diabetes mellitus comorbid conditions balanced data |