The Best-First (BF) Tree is a machine-learning algorithm employed for decision tree induction. It is an expansion of the conventional decision tree algorithm, which fuses the best-first search approach with the concept of pruning to enhance efficacy and precision.

Six common machine learning algorithms are the decision tree, the naïve Bayesian predictor, the random forest, the support vector machine, the artificial neural network, and the deep neural network (deep learning). A decision tree has three components: an intermediate node (a test on an independent variable), a branch (an outcome of the test ...

Apr 1, 2023 · This work illustrates the advantages of using machine learning classifiers in psychiatric assessment. Machine learning-decision trees (ML-DTs) represent a new approach to scoring and interpreting psychodiagnostic test data that allows for increasing assessment accuracy and efficiency.

Mar 31, 2022 · This review paper enlists different machine learning algorithms used to detect and diagnose depression. The ML-based depression detection algorithms are categorized into three classes...

Apr 24, 2025 · Depression has become one of the most common mental illnesses, causing severe physical and mental harm. To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning ...

Apr 25, 2025 · Depression; Human behaviour; Machine learning; Psychology; ... The algorithm constructs a binary decision tree by recursively partitioning the dataset based on predictor variables, selecting ...

In this paper, we have analyzed the different machine learning algorithms which can predict depression such as Decision Tree, Extra Trees (Ensemble Technique), Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (K-3), Naives Bayes are analyzed to find more accurate model to predict depression.

Machine learning algorithms are used to detect depression. This study has six different machine learning classifiers that use a variety of sociodemographic and psychosocial information to determine whether a person is depressed.

Machine learning algorithms used to classify the data and identify the depressive and non-depressive data. The purpose is to identify a user's depression using their data that is posted on social media. The Twitter data is then fed into multiple classifiers.

Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression. Keywords: Depression; Early diagnosis; Machine learning. To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder).