Machine learning-based predictive modelling of mental health in Rwandan Youth
Abstract Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental health outcomes by identifying patterns...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-00519-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849312119731781632 |
|---|---|
| author | Fauste Ndikumana Josias Izabayo Joseph Kalisa Mathieu Nemerimana Emmanuel Christian Nyabyenda Sylivain Hirwa Muzungu Isaac Komezusenge Melissa Uwase Similien Ndagijimana Celestin Twizere Vincent Sezibera |
| author_facet | Fauste Ndikumana Josias Izabayo Joseph Kalisa Mathieu Nemerimana Emmanuel Christian Nyabyenda Sylivain Hirwa Muzungu Isaac Komezusenge Melissa Uwase Similien Ndagijimana Celestin Twizere Vincent Sezibera |
| author_sort | Fauste Ndikumana |
| collection | DOAJ |
| description | Abstract Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental health outcomes by identifying patterns missed by traditional methods. However, its application in Rwanda remains under-explored. The study aims to apply machine learning techniques to predict mental health and identify its associated risk factors among Rwandan youth. Mental health data from Rwanda Biomedical Center, collected through the recent Rwanda mental health cross-sectional study and with youth sample of 5221 was used. We used four machine learning models namely logistic regression, Support Vector Machine, Random Forest and Gradient boosting to predict mental health vulnerability among youth. The research findings indicate that the random forest model is the most effective with an accuracy of 88.8% in modeling and predicting factors contributing to mental health vulnerability and 75 % in predicting mental disorders comorbidity. Exposure to traumatic events and violence, heavy drinking and a family history of mental health emerged as the most significant risk factors contributing to the development of mental disorders. While trauma experience, violence experience, affiliation to pro-social group and family history of mental disorders are the main comorbidity drivers. These findings indicate that machine learning can provide insightful results in predicting factors associated with mental health and confirm the role of social and biological factors in mental health. Therefore, it is crucial to consider biological and social factors particularly experience of violence and exposure to traumatic events, when developing mental health interventions and policies in Rwanda. Potential initiatives should prioritize the youth who experience social hardship to strengthen intervention efforts. |
| format | Article |
| id | doaj-art-70cbe3472ac64d29b9652450f34de83a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-70cbe3472ac64d29b9652450f34de83a2025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-00519-zMachine learning-based predictive modelling of mental health in Rwandan YouthFauste Ndikumana0Josias Izabayo1Joseph Kalisa2Mathieu Nemerimana3Emmanuel Christian Nyabyenda4Sylivain Hirwa Muzungu5Isaac Komezusenge6Melissa Uwase7Similien Ndagijimana8Celestin Twizere9Vincent Sezibera10African Center of Excellence in Data Sciences, University of RwandaCenter for Mental Health, University of RwandaCenter for Mental Health, University of RwandaMaternal, Newborn, Child and Adolescent Health Program, Partners In Health/Inshuti Mu BuzimaAfrican Center of Excellence in Data Sciences, University of RwandaAfrican Center of Excellence in Data Sciences, University of RwandaAfrican Center of Excellence in Data Sciences, University of RwandaCollege of Medicine and Health Sciences, University of RwandaCollege of Medicine and Health Sciences, University of RwandaThe Regional Centre of Excellence in Biomedical Engineering and EHealth, University of RwandaCenter for Mental Health, University of RwandaAbstract Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental health outcomes by identifying patterns missed by traditional methods. However, its application in Rwanda remains under-explored. The study aims to apply machine learning techniques to predict mental health and identify its associated risk factors among Rwandan youth. Mental health data from Rwanda Biomedical Center, collected through the recent Rwanda mental health cross-sectional study and with youth sample of 5221 was used. We used four machine learning models namely logistic regression, Support Vector Machine, Random Forest and Gradient boosting to predict mental health vulnerability among youth. The research findings indicate that the random forest model is the most effective with an accuracy of 88.8% in modeling and predicting factors contributing to mental health vulnerability and 75 % in predicting mental disorders comorbidity. Exposure to traumatic events and violence, heavy drinking and a family history of mental health emerged as the most significant risk factors contributing to the development of mental disorders. While trauma experience, violence experience, affiliation to pro-social group and family history of mental disorders are the main comorbidity drivers. These findings indicate that machine learning can provide insightful results in predicting factors associated with mental health and confirm the role of social and biological factors in mental health. Therefore, it is crucial to consider biological and social factors particularly experience of violence and exposure to traumatic events, when developing mental health interventions and policies in Rwanda. Potential initiatives should prioritize the youth who experience social hardship to strengthen intervention efforts.https://doi.org/10.1038/s41598-025-00519-zMachine learningPredictionMental healthYouthRwanda |
| spellingShingle | Fauste Ndikumana Josias Izabayo Joseph Kalisa Mathieu Nemerimana Emmanuel Christian Nyabyenda Sylivain Hirwa Muzungu Isaac Komezusenge Melissa Uwase Similien Ndagijimana Celestin Twizere Vincent Sezibera Machine learning-based predictive modelling of mental health in Rwandan Youth Scientific Reports Machine learning Prediction Mental health Youth Rwanda |
| title | Machine learning-based predictive modelling of mental health in Rwandan Youth |
| title_full | Machine learning-based predictive modelling of mental health in Rwandan Youth |
| title_fullStr | Machine learning-based predictive modelling of mental health in Rwandan Youth |
| title_full_unstemmed | Machine learning-based predictive modelling of mental health in Rwandan Youth |
| title_short | Machine learning-based predictive modelling of mental health in Rwandan Youth |
| title_sort | machine learning based predictive modelling of mental health in rwandan youth |
| topic | Machine learning Prediction Mental health Youth Rwanda |
| url | https://doi.org/10.1038/s41598-025-00519-z |
| work_keys_str_mv | AT faustendikumana machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT josiasizabayo machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT josephkalisa machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT mathieunemerimana machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT emmanuelchristiannyabyenda machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT sylivainhirwamuzungu machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT isaackomezusenge machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT melissauwase machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT similienndagijimana machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT celestintwizere machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth AT vincentsezibera machinelearningbasedpredictivemodellingofmentalhealthinrwandanyouth |