Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey

Hasib, Khan Md.; Islam, Rafiqul; Sakib, Shadman; Akbar, Md. Ali; Razzak, Muhammad Imran; Alam, Mohammad Shafiul

doi:10.1109/tcss.2023.3263128

Cited by 40 publications

(2 citation statements)

References 109 publications

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“…For classification of the imbalanced data in this study caused by the extremely low positive sample number in the data set, the cross validation and Smote technique (Synthetic minority over-sampling technique, Smote) were used to balance the data set and ensure excellent classification results in minority classes during model sampling, via retaining the majority class units and synthesizing new minority class units linearly from those that were set close ( 27 , 28 ). In RF modeling, the selected ensemble algorithms adopted the data classification strategy of constructing multiple weaker classifiers, combining them into classifiers with strong classifier generalization performance, and forcing the classifiers to focus on minority class samples in the algorithmic level, which is advantageous over the regular approach of establishing a single strong classifier with excellent generalization ability in the training set in terms of unbalanced data modeling ( 29 , 30 ). Besides, accuracy was not used as the single evaluation indicator in this study, because the overall accuracy of the imbalanced data classification would not accurately reflect the classification situation in minority classes.…”

Section: Discussionmentioning

confidence: 99%

Random forest algorithm for predicting postoperative delirium in older patients

Sheng,

Tang,

et al. 2024

Front. Neurol.

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ObjectiveIn this study, we were aimed to identify important variables via machine learning algorithms and predict postoperative delirium (POD) occurrence in older patients.MethodsThis study was to make the secondary analysis of data from a randomized controlled trial. The Boruta function was used to screen relevant basic characteristic variables. Four models including Logistic Regression (LR), K-Nearest Neighbor (KNN), the Classification and Regression Tree (CART), and Random Forest (RF) were established from the data set using repeated cross validation, hyper-parameter optimization, and Smote technique (Synthetic minority over-sampling technique, Smote), with the calculation of confusion matrix parameters and the plotting of Receiver operating characteristic curve (ROC), Precision recall curve (PRC), and partial dependence graph for further analysis and evaluation.ResultsThe basic characteristic variables resulting from Boruta screening included grouping, preoperative Mini-Mental State Examination(MMSE), CHARLSON score, preoperative HCT, preoperative serum creatinine, intraoperative bleeding volume, intraoperative urine volume, anesthesia duration, operation duration, postoperative morphine dosage, intensive care unit (ICU) duration, tracheal intubation duration, and 7-day postoperative rest and move pain score (median and max; VAS-Rest-M, VAS-Move-M, VAS-Rest-Max, and VAS-Move-Max). And Random Forest (RF) showed the best performance in the testing set among the 4 models with Accuracy: 0.9878; Matthews correlation coefficient (MCC): 0.8763; Area under ROC curve (AUC-ROC): 1.0; Area under the PRC Curve (AUC-PRC): 1.0.ConclusionA high-performance algorithm was established and verified in this study demonstrating the degree of POD risk changes in perioperative elderly patients. And the major risk factors for the development of POD were CREA and VAS-Move-Max.

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Section: Discussionmentioning

confidence: 99%

Random forest algorithm for predicting postoperative delirium in older patients

Sheng,

Tang,

et al. 2024

Front. Neurol.

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“…The paper [27] conducted a comprehensive examination of the DL and ML techniques used in the diagnosis of depression. They also emphasize the constraints of the current research efforts.…”

Section: Related Workmentioning

confidence: 99%

Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering

Mujahid,

Kına,

Rustam

et al. 2024

J Big Data

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The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. Overfitting of the model occurs as a result of imbalanced datasets, resulting in poor performance. In this study, we compare different oversampling techniques like synthetic minority oversampling technique (SMOTE), support vector machine SMOTE (SVM-SMOTE), Border-line SMOTE, K-means SMOTE, and adaptive synthetic (ADASYN) oversampling to address the issue of imbalanced datasets and enhance the performance of machine learning models. Preprocessing significantly enhances the quality of input data by reducing noise, redundant data, and unnecessary data. This enables the machines to identify crucial patterns that facilitate the extraction of significant and pertinent information from the preprocessed data. This study preprocesses the data using various top-level preprocessing steps. Furthermore, two imbalanced Twitter datasets are used to compare the performance of oversampling techniques with six machine learning models including random forest (RF), SVM, K-nearest neighbor (KNN), AdaBoost (ADA), logistic regression (LR), and decision tree (DT). In addition, the bag of words (BoW) and term frequency and inverse document frequency (TF-IDF) features extraction approaches are used to extract features from the tweets. The experiments indicate that SMOTE and ADASYN perform much better than other techniques thus providing higher accuracy. Additionally, overall results show that SVM with ’linear’ kernel tends to attain the highest accuracy and recall score of 99.67% and 1.00% on ADASYN oversampled datasets and 99.57% accuracy on SMOTE oversampled dataset with TF-IDF features. The SVM model using 10-fold cross-validation experiments achieved 97.40 mean accuracy with a 0.008 standard deviation. Our approach achieved 2.62% greater accuracy as compared to other current methods.

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A More Effective Ensemble ML Method for Detecting Breast Cancer

Ferdous

2024

Algorithms for Intelligent Systems

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Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey

Cited by 40 publications

References 109 publications

Random forest algorithm for predicting postoperative delirium in older patients

Random forest algorithm for predicting postoperative delirium in older patients

Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering

A More Effective Ensemble ML Method for Detecting Breast Cancer

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