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The main objective of this study was to find a preprocessing method to enhance the effectiveness of the machine learning methods in datasets of mental patients. Specifically, the machine learning methods must have almost excellent classification results in patients with depression who have thoughts of suicide, in order to achieve the sooner the possible the appropriate treatment. In this paper, we establish a novel data preprocessing method for improving the prognosis' possibilities of a patient suffering from depression to be leaded to the suicide. For this reason, the effectiveness of many machine learning classification algorithms is measured, with and without the use of our suggested preprocessing method. The experimental results reveal that our novel proposed data preprocessing method markedly improved the overall performance on initial dataset comparing with PCA and Evolutionary search feature selection methods. So this preprocessing method can be used for significantly boost classification algorithms performance in similar datasets and can be used for suicide tendency prediction.

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Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After examining the distributions of AUC scores in all cases, non-linear models appear to be superior to baseline linear models. Moreover, apart from personalized approaches, group-level prediction models are also likely to offer an enhanced performance. According to this, two different nomothetic approaches to integrate data of more than one individuals are examined, one using directly all data during training and one based on knowledge distillation. Interestingly, it is observed that in one of the two real-world datasets, knowledge distillation method achieves improved AUC scores (mean relative change of +17% compared to personalized) showing how it can benefit EMA data classification and performance.