Enhancing mental well-being: An artificial intelligence model for predicting mental disorders
Pervasive mental health conditions like depression, stress, and anxiety significantly affect individual well-being. Early identification and understanding are critical to minimize their negative effects. This study explores how AI models can be leveraged for improved mental health assessment. We hav...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Array |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259000562500044X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849422255195422720 |
|---|---|
| author | Jahanur Biswas Md. Nahid Hasan Md. Shakil Rahman Gazi Md. Mahbubur Rahman |
| author_facet | Jahanur Biswas Md. Nahid Hasan Md. Shakil Rahman Gazi Md. Mahbubur Rahman |
| author_sort | Jahanur Biswas |
| collection | DOAJ |
| description | Pervasive mental health conditions like depression, stress, and anxiety significantly affect individual well-being. Early identification and understanding are critical to minimize their negative effects. This study explores how AI models can be leveraged for improved mental health assessment. We have introduced some machine learning classifiers along with a deep learning model. Among the applied ML classifiers, the ”Ensemble” approach aims to outperform individual models by harnessing their collective strengths. While data acquisition for mental health research can be challenging, we utilized a publicly available dataset from the Kaggle site, acknowledging potential limitations like data imbalances and missing values. This imbalanced dataset is balanced by the Random Oversampling model. In our study, we introduced a state-of-the-art approach to predicting mental conditions such as depression, anxiety, and stress. For every condition, we utilized three machine learning models: the K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), combining these models into an ensemble model where a Voting classifier is used in the ensemble model. We also applied the Artificial Neural network (ANN) as a deep learning model for each disorder. Though the ensemble model performed an excellent outcome, the ANN model found outstanding outcomes from all the models. The ANN model achieved the highest accuracy of 99.73% for depression, 99.89% for anxiety, and 99.39% for stress. |
| format | Article |
| id | doaj-art-c9e888b853d042cf88d3d8a10be1341c |
| institution | Kabale University |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj-art-c9e888b853d042cf88d3d8a10be1341c2025-08-20T03:31:11ZengElsevierArray2590-00562025-07-012610041710.1016/j.array.2025.100417Enhancing mental well-being: An artificial intelligence model for predicting mental disordersJahanur Biswas0Md. Nahid Hasan1Md. Shakil Rahman Gazi2Md. Mahbubur Rahman3Department of CSE, Dhaka International University, Satarkul Rd, Dhaka 1212, Dhaka, BangladeshDepartment of CSE, Dhaka International University, Satarkul Rd, Dhaka 1212, Dhaka, Bangladesh; Corresponding author.Department of CSE, Dhaka International University, Satarkul Rd, Dhaka 1212, Dhaka, BangladeshDepartment of CSE, Bangladesh University of Business and Technology, Plot # 77-78, 2 Road No. 7, Dhaka 1216, Dhaka, BangladeshPervasive mental health conditions like depression, stress, and anxiety significantly affect individual well-being. Early identification and understanding are critical to minimize their negative effects. This study explores how AI models can be leveraged for improved mental health assessment. We have introduced some machine learning classifiers along with a deep learning model. Among the applied ML classifiers, the ”Ensemble” approach aims to outperform individual models by harnessing their collective strengths. While data acquisition for mental health research can be challenging, we utilized a publicly available dataset from the Kaggle site, acknowledging potential limitations like data imbalances and missing values. This imbalanced dataset is balanced by the Random Oversampling model. In our study, we introduced a state-of-the-art approach to predicting mental conditions such as depression, anxiety, and stress. For every condition, we utilized three machine learning models: the K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), combining these models into an ensemble model where a Voting classifier is used in the ensemble model. We also applied the Artificial Neural network (ANN) as a deep learning model for each disorder. Though the ensemble model performed an excellent outcome, the ANN model found outstanding outcomes from all the models. The ANN model achieved the highest accuracy of 99.73% for depression, 99.89% for anxiety, and 99.39% for stress.http://www.sciencedirect.com/science/article/pii/S259000562500044XEnsemble modelMental disorderArtificial neural networkMental well-beingAnxietyDepression |
| spellingShingle | Jahanur Biswas Md. Nahid Hasan Md. Shakil Rahman Gazi Md. Mahbubur Rahman Enhancing mental well-being: An artificial intelligence model for predicting mental disorders Array Ensemble model Mental disorder Artificial neural network Mental well-being Anxiety Depression |
| title | Enhancing mental well-being: An artificial intelligence model for predicting mental disorders |
| title_full | Enhancing mental well-being: An artificial intelligence model for predicting mental disorders |
| title_fullStr | Enhancing mental well-being: An artificial intelligence model for predicting mental disorders |
| title_full_unstemmed | Enhancing mental well-being: An artificial intelligence model for predicting mental disorders |
| title_short | Enhancing mental well-being: An artificial intelligence model for predicting mental disorders |
| title_sort | enhancing mental well being an artificial intelligence model for predicting mental disorders |
| topic | Ensemble model Mental disorder Artificial neural network Mental well-being Anxiety Depression |
| url | http://www.sciencedirect.com/science/article/pii/S259000562500044X |
| work_keys_str_mv | AT jahanurbiswas enhancingmentalwellbeinganartificialintelligencemodelforpredictingmentaldisorders AT mdnahidhasan enhancingmentalwellbeinganartificialintelligencemodelforpredictingmentaldisorders AT mdshakilrahmangazi enhancingmentalwellbeinganartificialintelligencemodelforpredictingmentaldisorders AT mdmahbuburrahman enhancingmentalwellbeinganartificialintelligencemodelforpredictingmentaldisorders |