Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm

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Abstract

The presented research responds to increased mental illness conditions worldwide and the need for efficient mental health care (MHC) through machine learning (ML) implementations. The datasets employed in this investigation belong to a Kaggle repository named "Mental Health Tech Survey." The surveys for the years 2014 and 2016 were downloaded and aggregated. The prediction results for bagging, stacking, LR, KNN, tree class, NN, RF, and Adaboost yielded 75.93%, 75.93%, 79.89%, 90.42%, 80.69%, 89.95%, 81.22%, and 81.75% respectively. The AdaBoost ML model performed data cleaning and prediction on the datasets, reaching an accuracy of 81.75%, which is good enough for decision-making. The results were further used with other ML models such as Random Forest (RF), K-Nearest Neighbor (KNN), bagging, and a few others, with reported accuracy ranging from 81.22 to 75.93 which is good enough for decision making. Out of all the models used for predicting mental health treatment outcomes, AdaBoost has the highest accuracy.

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