A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms

E. Syed Mohamed, Tawseef Ahmad Naqishbandi*, Syed Ahmad Chan Bukhari, Insha Rauf, Vilas Sawrikar, Arshad Hussain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Article number100185
Pages (from-to)1-20
Number of pages20
JournalHealthcare Analytics
Early online date2 May 2023
Publication statusPublished - Nov 2023

Keywords / Materials (for Non-textual outputs)

  • machine learning
  • mental health
  • Multilayer Perceptron
  • prediction
  • Random Forest
  • Support Vector Machine


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