Confusion Matrix for Binary Classification.
<div><p>Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, and emptiness. Traditional self-report and experimental methods rely on autobiographical recall and are therefore vulnerable to bi...
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2025
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| _version_ | 1849927628986777600 |
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| author | Chan-Young Ahn (22683343) |
| author2 | Jin-Ha Kim (10300456) Sojung Kim (46675) Jae-Won Kim (172504) Jung-Jo Na (22683346) Dong Gi Seo (11119119) Jong-Sun Lee (4252801) |
| author2_role | author author author author author author |
| author_facet | Chan-Young Ahn (22683343) Jin-Ha Kim (10300456) Sojung Kim (46675) Jae-Won Kim (172504) Jung-Jo Na (22683346) Dong Gi Seo (11119119) Jong-Sun Lee (4252801) |
| author_role | author |
| dc.creator.none.fl_str_mv | Chan-Young Ahn (22683343) Jin-Ha Kim (10300456) Sojung Kim (46675) Jae-Won Kim (172504) Jung-Jo Na (22683346) Dong Gi Seo (11119119) Jong-Sun Lee (4252801) |
| dc.date.none.fl_str_mv | 2025-11-25T18:27:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320104.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Confusion_Matrix_for_Binary_Classification_/30713650 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Sociology Mental Health Biological Sciences not elsewhere classified xlink "> non providing practical insights multilevel logistic regression low ecological validity justifying multilevel modeling experimental methods rely ecological momentary assessment address social connections 26 indicated substantial identify emotional predictors adolescents &# 8217 repeatedly sample nssi significant individual differences combining machine learning anger towards others experiencing nssi thoughts 40 ), anxiety findings highlight loneliness significant predictors machine learning nssi thoughts individual variance adolescents engaging 18 ), therefore vulnerable study aimed smartphone application related feelings past year participants reported often linked negative emotions integrated application influential predictor icc value feature importance developing tailored daily life autobiographical recall 15 years |
| dc.title.none.fl_str_mv | Confusion Matrix for Binary Classification. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, and emptiness. Traditional self-report and experimental methods rely on autobiographical recall and are therefore vulnerable to bias and low ecological validity. Accordingly, approaches that repeatedly sample NSSI-related feelings and contexts in daily life such as Ecological Momentary Assessment (EMA) are needed. This study aimed to identify emotional predictors of NSSI thoughts among adolescents using machine learning and multilevel logistic regression. The study included 42 adolescents (aged 12–15 years) who had engaged in NSSI in the past year. Participants reported their mood and NSSI behaviors three times daily over a 14-day EMA period via a smartphone application. Predictor variables included depression, anxiety, loneliness, self-anger, anger towards others, shame, and emptiness. A random forest model identified loneliness (feature importance: 0.40), anxiety (0.18), and emptiness (0.14) as the most significant predictors of NSSI thoughts. Multilevel logistic regression confirmed these findings, showing that each one-unit increase in anxiety, loneliness, and emptiness corresponded to a 24%, 19%, and 24% increase in the odds of experiencing NSSI thoughts, respectively. The ICC value of 0.26 indicated substantial between-individual variance, justifying multilevel modeling. However, random effects analysis revealed no significant individual differences, suggesting uniform effects across participants. These findings highlight loneliness as the most influential predictor, emphasizing the need to address social connections in interventions. Combining machine learning with traditional statistical methods enhanced interpretability, providing practical insights for developing tailored, emotion-focused interventions for adolescents engaging in NSSI.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_a36a91050b92c819dd5b107b91dcae1f |
| identifier_str_mv | 10.1371/journal.pone.0320104.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30713650 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Confusion Matrix for Binary Classification.Chan-Young Ahn (22683343)Jin-Ha Kim (10300456)Sojung Kim (46675)Jae-Won Kim (172504)Jung-Jo Na (22683346)Dong Gi Seo (11119119)Jong-Sun Lee (4252801)MedicineSociologyMental HealthBiological Sciences not elsewhere classifiedxlink "> nonproviding practical insightsmultilevel logistic regressionlow ecological validityjustifying multilevel modelingexperimental methods relyecological momentary assessmentaddress social connections26 indicated substantialidentify emotional predictorsadolescents &# 8217repeatedly sample nssisignificant individual differencescombining machine learninganger towards othersexperiencing nssi thoughts40 ), anxietyfindings highlight lonelinesssignificant predictorsmachine learningnssi thoughtsindividual varianceadolescents engaging18 ),therefore vulnerablestudy aimedsmartphone applicationrelated feelingspast yearparticipants reportedoften linkednegative emotionsintegrated applicationinfluential predictoricc valuefeature importancedeveloping tailoreddaily lifeautobiographical recall15 years<div><p>Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, and emptiness. Traditional self-report and experimental methods rely on autobiographical recall and are therefore vulnerable to bias and low ecological validity. Accordingly, approaches that repeatedly sample NSSI-related feelings and contexts in daily life such as Ecological Momentary Assessment (EMA) are needed. This study aimed to identify emotional predictors of NSSI thoughts among adolescents using machine learning and multilevel logistic regression. The study included 42 adolescents (aged 12–15 years) who had engaged in NSSI in the past year. Participants reported their mood and NSSI behaviors three times daily over a 14-day EMA period via a smartphone application. Predictor variables included depression, anxiety, loneliness, self-anger, anger towards others, shame, and emptiness. A random forest model identified loneliness (feature importance: 0.40), anxiety (0.18), and emptiness (0.14) as the most significant predictors of NSSI thoughts. Multilevel logistic regression confirmed these findings, showing that each one-unit increase in anxiety, loneliness, and emptiness corresponded to a 24%, 19%, and 24% increase in the odds of experiencing NSSI thoughts, respectively. The ICC value of 0.26 indicated substantial between-individual variance, justifying multilevel modeling. However, random effects analysis revealed no significant individual differences, suggesting uniform effects across participants. These findings highlight loneliness as the most influential predictor, emphasizing the need to address social connections in interventions. Combining machine learning with traditional statistical methods enhanced interpretability, providing practical insights for developing tailored, emotion-focused interventions for adolescents engaging in NSSI.</p></div>2025-11-25T18:27:17ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0320104.t003https://figshare.com/articles/dataset/Confusion_Matrix_for_Binary_Classification_/30713650CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307136502025-11-25T18:27:17Z |
| spellingShingle | Confusion Matrix for Binary Classification. Chan-Young Ahn (22683343) Medicine Sociology Mental Health Biological Sciences not elsewhere classified xlink "> non providing practical insights multilevel logistic regression low ecological validity justifying multilevel modeling experimental methods rely ecological momentary assessment address social connections 26 indicated substantial identify emotional predictors adolescents &# 8217 repeatedly sample nssi significant individual differences combining machine learning anger towards others experiencing nssi thoughts 40 ), anxiety findings highlight loneliness significant predictors machine learning nssi thoughts individual variance adolescents engaging 18 ), therefore vulnerable study aimed smartphone application related feelings past year participants reported often linked negative emotions integrated application influential predictor icc value feature importance developing tailored daily life autobiographical recall 15 years |
| status_str | publishedVersion |
| title | Confusion Matrix for Binary Classification. |
| title_full | Confusion Matrix for Binary Classification. |
| title_fullStr | Confusion Matrix for Binary Classification. |
| title_full_unstemmed | Confusion Matrix for Binary Classification. |
| title_short | Confusion Matrix for Binary Classification. |
| title_sort | Confusion Matrix for Binary Classification. |
| topic | Medicine Sociology Mental Health Biological Sciences not elsewhere classified xlink "> non providing practical insights multilevel logistic regression low ecological validity justifying multilevel modeling experimental methods rely ecological momentary assessment address social connections 26 indicated substantial identify emotional predictors adolescents &# 8217 repeatedly sample nssi significant individual differences combining machine learning anger towards others experiencing nssi thoughts 40 ), anxiety findings highlight loneliness significant predictors machine learning nssi thoughts individual variance adolescents engaging 18 ), therefore vulnerable study aimed smartphone application related feelings past year participants reported often linked negative emotions integrated application influential predictor icc value feature importance developing tailored daily life autobiographical recall 15 years |