Early detection of mental health disorders using machine learning models using behavioral and voice data analysis
Abstract People of all demographics are impacted by mental illness, which has become a widespread and international health problem. Effective treatment and support for mental illnesses depend on early discovery and precise diagnosis. Notably, delayed diagnosis may lead to suicidal thoughts, destruct...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00386-8 |
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| author | Sunil Kumar Sharma Ahmed Ibrahim Alutaibi Ahmad Raza Khan Ghanshyam G. Tejani Fuzail Ahmad Seyed Jalaleddin Mousavirad |
| author_facet | Sunil Kumar Sharma Ahmed Ibrahim Alutaibi Ahmad Raza Khan Ghanshyam G. Tejani Fuzail Ahmad Seyed Jalaleddin Mousavirad |
| author_sort | Sunil Kumar Sharma |
| collection | DOAJ |
| description | Abstract People of all demographics are impacted by mental illness, which has become a widespread and international health problem. Effective treatment and support for mental illnesses depend on early discovery and precise diagnosis. Notably, delayed diagnosis may lead to suicidal thoughts, destructive behaviour, and death. Manual diagnosis is time-consuming and laborious. With the advent of AI, this research aims to develop a novel mental health disorder detection network with the objective of maximum accuracy and early discovery. For this reason, this study presents a novel framework for the early detection of mental illness disorders using a multi-modal approach combining speech and behavioral data. This framework preprocesses and analyzes two distinct datasets to handle missing values, normalize data, and eliminate outliers. The proposed NeuroVibeNet combines Improved Random Forest (IRF) and Light Gradient-Boosting Machine (LightGBM) for behavioral data and Hybrid Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for voice data. Finally, a weighted voting mechanism is applied to consolidate predictions. The proposed model achieves robust performance and a competitive accuracy of 99.06% in distinguishing normal and pathological conditions. This framework validates the feasibility of multi-modal data integration for reliable and early mental illness detection. |
| format | Article |
| id | doaj-art-7804b6ede2a04a40a531000e1a05b9df |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7804b6ede2a04a40a531000e1a05b9df2025-08-20T03:53:57ZengNature PortfolioScientific Reports2045-23222025-05-0115111910.1038/s41598-025-00386-8Early detection of mental health disorders using machine learning models using behavioral and voice data analysisSunil Kumar Sharma0Ahmed Ibrahim Alutaibi1Ahmad Raza Khan2Ghanshyam G. Tejani3Fuzail Ahmad4Seyed Jalaleddin Mousavirad5Department of Information Systems, College of Computer and Information Sciences, Majmaah UniversityDepartment of Computer Engineering, College of Computer and Information Sciences, Majmaah UniversityInformation Technology Department, College of Computer and Information Sciences Majmaah UniversityDepartment of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha UniversityApplied Science Research Center, Applied Science Private UniversityDepartment of Computer and Electrical Engineering, Mid Sweden UniversityAbstract People of all demographics are impacted by mental illness, which has become a widespread and international health problem. Effective treatment and support for mental illnesses depend on early discovery and precise diagnosis. Notably, delayed diagnosis may lead to suicidal thoughts, destructive behaviour, and death. Manual diagnosis is time-consuming and laborious. With the advent of AI, this research aims to develop a novel mental health disorder detection network with the objective of maximum accuracy and early discovery. For this reason, this study presents a novel framework for the early detection of mental illness disorders using a multi-modal approach combining speech and behavioral data. This framework preprocesses and analyzes two distinct datasets to handle missing values, normalize data, and eliminate outliers. The proposed NeuroVibeNet combines Improved Random Forest (IRF) and Light Gradient-Boosting Machine (LightGBM) for behavioral data and Hybrid Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for voice data. Finally, a weighted voting mechanism is applied to consolidate predictions. The proposed model achieves robust performance and a competitive accuracy of 99.06% in distinguishing normal and pathological conditions. This framework validates the feasibility of multi-modal data integration for reliable and early mental illness detection.https://doi.org/10.1038/s41598-025-00386-8Mental health disordersDeep learningBehavioral dataVoice dataMachine learning |
| spellingShingle | Sunil Kumar Sharma Ahmed Ibrahim Alutaibi Ahmad Raza Khan Ghanshyam G. Tejani Fuzail Ahmad Seyed Jalaleddin Mousavirad Early detection of mental health disorders using machine learning models using behavioral and voice data analysis Scientific Reports Mental health disorders Deep learning Behavioral data Voice data Machine learning |
| title | Early detection of mental health disorders using machine learning models using behavioral and voice data analysis |
| title_full | Early detection of mental health disorders using machine learning models using behavioral and voice data analysis |
| title_fullStr | Early detection of mental health disorders using machine learning models using behavioral and voice data analysis |
| title_full_unstemmed | Early detection of mental health disorders using machine learning models using behavioral and voice data analysis |
| title_short | Early detection of mental health disorders using machine learning models using behavioral and voice data analysis |
| title_sort | early detection of mental health disorders using machine learning models using behavioral and voice data analysis |
| topic | Mental health disorders Deep learning Behavioral data Voice data Machine learning |
| url | https://doi.org/10.1038/s41598-025-00386-8 |
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