A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms
The prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian government to impose safety precautions such as l...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523000527 |
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| author | E. Syed Mohamed Tawseef Ahmad Naqishbandi Syed Ahmad Chan Bukhari Insha Rauf Vilas Sawrikar Arshad Hussain |
| author_facet | E. Syed Mohamed Tawseef Ahmad Naqishbandi Syed Ahmad Chan Bukhari Insha Rauf Vilas Sawrikar Arshad Hussain |
| author_sort | E. Syed Mohamed |
| collection | DOAJ |
| description | The prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian government to impose safety precautions such as lockdowns and communication ban. Consequently, the region of Kashmir experienced a marked rise in anxiety as a result of these lifestyle changes. Machine learning has proven useful in the early diagnosis and prognosis of certain diseases. Therefore, this study aims to classify anxiety problems early by utilising a pre-clinical mental health dataset collected after the abrogation of article 370 in Kashmir. The first part of the paper aims at developing and implementing a prediction model based on classification into one of the five pre-clinical anxiety stages, i.e., Stage 1: minimal anxiety, Stage 2: mild anxiety, Stage 3: moderate anxiety, Stage 4: severe anxiety, and Stage 5: very severe anxiety. The second part offers recommendations for those suffering from anxiety disorders. Feature selection and prediction are used to predict the correct stage of anxiety for best possible medical intervention. Three different algorithms: Support Vector Machine(SVM), Multilayer Perceptron (MLP), and Random Forest (RF), are employed for predicting anxiety stages. Among them, random forest (RF) achieved 98.13% accuracy. A forecasted likelihood condition was assessed to provide a suitable recommendation. Further, accuracy and kappa statistics are used to assess the performance of the suggested model, which offers a significant addition to predicting anxiety early, and exhibits high prediction and recommendation accuracy. This study aims to assist mental health professionals and experts in making quick and accurate choices. |
| first_indexed | 2024-03-13T03:27:29Z |
| format | Article |
| id | doaj.art-8b9c354bb5d245ae83489c89d046d6b9 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-13T03:27:29Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-8b9c354bb5d245ae83489c89d046d6b92023-06-25T04:44:19ZengElsevierHealthcare Analytics2772-44252023-11-013100185A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithmsE. Syed Mohamed0Tawseef Ahmad Naqishbandi1Syed Ahmad Chan Bukhari2Insha Rauf3Vilas Sawrikar4Arshad Hussain5Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, IndiaDepartment of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India; Corresponding author.Division of Computer Science, Mathematics and Science, Collins College of Professional Studies St. John’s University, NY, United States of AmericaDepartment of Psychiatry, Government Medical College Srinagar, Jammu and Kashmir 190010, IndiaDepartment of Clinical and Health Psychology, University of Edinburgh, Edinburgh, United KingdomDepartment of Psychiatry, Government Medical College Srinagar, Jammu and Kashmir 190010, IndiaThe prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian government to impose safety precautions such as lockdowns and communication ban. Consequently, the region of Kashmir experienced a marked rise in anxiety as a result of these lifestyle changes. Machine learning has proven useful in the early diagnosis and prognosis of certain diseases. Therefore, this study aims to classify anxiety problems early by utilising a pre-clinical mental health dataset collected after the abrogation of article 370 in Kashmir. The first part of the paper aims at developing and implementing a prediction model based on classification into one of the five pre-clinical anxiety stages, i.e., Stage 1: minimal anxiety, Stage 2: mild anxiety, Stage 3: moderate anxiety, Stage 4: severe anxiety, and Stage 5: very severe anxiety. The second part offers recommendations for those suffering from anxiety disorders. Feature selection and prediction are used to predict the correct stage of anxiety for best possible medical intervention. Three different algorithms: Support Vector Machine(SVM), Multilayer Perceptron (MLP), and Random Forest (RF), are employed for predicting anxiety stages. Among them, random forest (RF) achieved 98.13% accuracy. A forecasted likelihood condition was assessed to provide a suitable recommendation. Further, accuracy and kappa statistics are used to assess the performance of the suggested model, which offers a significant addition to predicting anxiety early, and exhibits high prediction and recommendation accuracy. This study aims to assist mental health professionals and experts in making quick and accurate choices.http://www.sciencedirect.com/science/article/pii/S2772442523000527Mental healthMachine learningPredictionSupport Vector MachineMultilayer PerceptronRandom Forest |
| spellingShingle | E. Syed Mohamed Tawseef Ahmad Naqishbandi Syed Ahmad Chan Bukhari Insha Rauf Vilas Sawrikar Arshad Hussain A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms Healthcare Analytics Mental health Machine learning Prediction Support Vector Machine Multilayer Perceptron Random Forest |
| title | A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms |
| title_full | A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms |
| title_fullStr | A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms |
| title_full_unstemmed | A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms |
| title_short | A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms |
| title_sort | hybrid mental health prediction model using support vector machine multilayer perceptron and random forest algorithms |
| topic | Mental health Machine learning Prediction Support Vector Machine Multilayer Perceptron Random Forest |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523000527 |
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