Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning

Yao, Lijun; Wang, Ziyi; Gu, Hong; Zhao, Xudong; Chen, Yang; Liu, Liang

doi:10.3389/fpsyt.2023.947081

Cited by 4 publications

(3 citation statements)

References 75 publications

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“…Therefore, if mobile operators want to improve user satisfaction, they need to improve these seven core impact factors, including whether they have encountered network problems, residential area, underground, no signal of mobile phone, sudden interruption during the call, inaudible intermittent and intermittent call with noise, and one party cannot be heard during the call. (2) General machine learning prediction algorithms have an accuracy of about 70% [23,40,41], and the prediction accuracy of this study can reach 99.96%, which is a very high accuracy when predicting. Highly accurate satisfaction prediction can help operators more accurately adjust their operational strategies, so as to improve their market competitiveness.…”

Section: Discussionmentioning

confidence: 75%

Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration

Zeng,

He,

Yang

et al. 2023

Applied Sciences

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Customer satisfaction is a measure of the degree of satisfaction of customer experience. Among the three major operators in China, China Mobile plays an important role in the communication field. A study of customer satisfaction with China Mobile will have a significant positive impact on the sustainable development of the entire communication industry. In order to respond to customer needs accurately, a mobile customer satisfaction research method based on quadratic dimensionality reduction and machine learning integration is proposed. Firstly, the core evaluation system of impact satisfaction is established, through the integration of systematic clustering and exploratory factor analysis for quadratic dimensionality reduction. Then, unreasonable data in the core influencing factors are eliminated. Finally, the gradient-boosted decision tree (GBDT) machine learning algorithm is applied to predict satisfaction, with a prediction accuracy of up to 99%, and the highly accurate satisfaction prediction can quickly respond to customer needs and feedback to improve customer experience and satisfaction.

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Section: Discussionmentioning

confidence: 75%

Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration

Zeng,

He,

Yang

et al. 2023

Applied Sciences

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“…Additionally, ML has the potential to benefit mental healthcare as it can account for the interaction between many features ( 137 ). The ML techniques are suitable to detect features with the strongest predictive influence in different contexts and mutual interactions, thereby providing a combined measure of both individual and multivariate impact of each feature ( 138 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

Franken,

ten Klooster,

Bohlmeijer

et al. 2023

Front. Psychiatry

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ObjectivesAnxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.MethodsIn the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.ResultsML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models’ performance (AUC range: 0.68–0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58–0.62 vs. 0.58–0.63) and models with early change scores (AUC: 0.69–0.73 vs. 0.68–0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.ConclusionAccurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress.

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“…The three most obvious factors that determine whether clients are satisfied with psychotherapy are the occupation of the clients, the location of psychotherapy, and the method of access to psychotherapy. With an F1 score of 0.758, the machine-learning model built on the CatBoost algorithm classified satisfied and psychotherapy clients with the maximum degree of accuracy [11].…”

Section: Customer Satisfaction Predictionmentioning

confidence: 99%

Prediction of US airline passenger satisfaction using machine learning algorithms

Hong

Khaw

Chew

et al. 2023

DAAM

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Due to the COVID-19 pandemic, the U.S. financial system and economy have also been severely affected. The U.S. airline industry has been hit particularly hard by the COVID-19 pandemic. Additionally, the aviation industry is also full of competition. One of the ways to attract customers and compete with other airline companies is by improving their service quality. Therefore, this study aims to predict the satisfaction of airlines based on the machine learning model and discover which features are more correlated with the target variable. In this study, the dataset consists of 129,880 observations and 1 target, 22 features or attributes (not including the identification). In this study, the result showed that the features that slightly correlate more with customer satisfaction are 'Online boarding', 'Inflight entertainment', 'Seat comfort', 'On-board service', 'Leg room service', 'Cleanliness', 'Flight Distance' and 'Inflight wifi service'. Then, K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and AdaBoost were used to build the classification models. Data cleaning, exploratory data analysis, feature selection and One Hot Encoding were also performed before building the models. Finally, the models were evaluated based on their accuracy, precision, recall and F1-score. The results suggest that the champion model for this study is Random Forest, which achieved 89.20% accuracy, 93.04% precision and 88.80% F1-score. The results of this study can be used as a guide in applying machine learning to predict the satisfaction of airline passengers. This can also contribute to attracting passengers by improving the airline service quality.

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Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning

Cited by 4 publications

References 75 publications

Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration

Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

Prediction of US airline passenger satisfaction using machine learning algorithms

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