Mental Health Prediction Using Catboost Algorithm

Abstract

This study investigates the application of the CatBoost algorithm in predicting mental health outcomes usingPythonprogramminglanguage.Mentalhealthpredictionisacriticalareaofresearch due to its significant impacton individuals andsociety. Traditional predictivemodelingtechniques often encounter challengesinhandlingcomplexandhigh-dimensionaldatainherentinmentalhealthdatasets.CatBoost,a state-of-the-artgradientboostingalgorithm,hasshownpromiseineffectivelyaddressingthesechallenges byhandlingcategoricalvariablesseamlesslyandexhibitingrobustperformancein variousdomains.Leveragingits powerful capabilities,this studyaims to developpredictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural,andclinicalfactors.ThepredictiveperformanceoftheCatBoostalgorithmwillbeevaluatedandcomparedagainstother commonlyusedmachinelearningalgorithms,demonstratingitseffectiveness inaccuratelypredictingmental healthoutcomes.Thisresearchcontributestotheadvancementof predictivemodelinginmentalhealth research and holds potential implications for personalized interventions and resource allocation in mentalhealthcaresystems.

Introduction

The study focuses on developing a predictive model for mental health outcomes using the CatBoost machine learning algorithm, which is effective in handling categorical data and imbalanced datasets common in mental health research. Implemented in Python with the Flask framework, the model analyzes socio-demographic, behavioral, and clinical factors to predict individuals' risk of developing mental health issues. The aim is to support early detection and intervention through an accessible web application.

The methodology involves using a dataset of student mental health data from Kaggle, with preprocessing steps like label encoding and data splitting for training, validation, and testing. CatBoost is chosen for its robust handling of categorical features and gradient boosting capabilities. The model undergoes hyperparameter tuning, training, and evaluation using metrics such as accuracy, precision, recall, and F1 score.

The results classify mental health status into three levels: low, moderate, and high stress. Future enhancements include expanding datasets to improve generalizability, integrating additional data sources like social media sentiment and wearable device inputs, and applying interpretability tools (e.g., SHAP, LIME) to increase model transparency and user trust.

Conclusion

Insummary,ourintegrationoftheCatBoostalgorithmintotheflaskframeworkformentalhealthpredictionexhibitsencouraging outcomesandpotentialapplicationswithintherealmofmentalhealthcare.Byemployingrobustmachinelearningtechniquesand flask\'suser-friendlyinterface,wehavedevelopedatoolproficientinpredictingmentalhealthconditionsbasedonpertinentinput features. The CatBoost algorithm, renowned for its adeptness in handling categorical features androbust performance in predictive tasks, forms a dependable foundation for our prediction model. Through its utilization, we have attained precise predictions while mitigating the risk of overfitting and enhancing interpretability. Conversely, flask furnishes an instinctive and interactive platform for users to input their data and acquire real-time predictions. Itssimplicityandcustomizableattributesrenderitanoptimalchoicefordeployingmachinelearningmodelsandpresentingtheir outcomes to diverse audiences, including healthcare professionals and individuals in need of assistance.

References

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Copyright

Copyright © 2025 Mr. M. Jeevan Babu, Sirisha Kamunuri, Bhavana Sri Keerthi Jarugu, Srisaiteja Guttikonda, Venkata Srinivasa Rao Kesanam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.