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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.

BackgroundAs the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals’ risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services. ObjectiveThe aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. MethodsWe conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm’s allocation based on a multidimensional self-report questionnaire. ResultsOf the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts’ ratings, with a substantial interrater agreement (κ=0.67; P