Early Detection of Mental Health Issues through Machine Learning: A Comparative Analysis of Predictive Models

doi:10.21203/rs.3.rs-4952776/v1

Mental health is a pivotal aspect of human well-being, yet it remains under-recognized and stigmatized in many societies. This study explores the integration of artificial intelligence and machine learning techniques to enhance the early detection and management of mental health disorders. By analyzing comprehensive datasets encompassing sociodemographic, medical, and environmental factors, we developed and evaluated several predictive models. Our approach included logistic regression, K-nearest neighbors, decision trees, random forests, bagging, boosting, stacking, and neural networks. The models were trained and tested to predict mental health issues, with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that ensemble methods and neural networks outperform traditional algorithms in predicting mental health conditions, offering a promising direction for early intervention and proactive mental health management. This research highlights the potential of machine learning to transform mental health care by providing accurate, data-driven insights for early detection and intervention.

Biological sciences/Neuroscience

Health sciences/Health care

Physical sciences/Engineering

Mental Health Detection

Machine Learning

Predictive Models

Ensemble Methods

Neural Networks

Mental health is a cornerstone of human well-being, influencing our ability to lead fulfilling lives and positively contribute to society. However, despite its undeniable significance, mental health often remains neglected and shrouded in stigma in many societies. Mental disorders, such as depression, anxiety, and bipolar disorders, pose major challenges that affect individuals' daily lives and the overall functioning of society (Burke et al., 2019).

In the face of these challenges, it becomes imperative to develop effective approaches to detect, assess, and treat mental health issues early and accurately. In this context, advances in the field of artificial intelligence (AI) and data science offer promising opportunities to address these critical needs. By harnessing vast amounts of available data and applying sophisticated algorithms, it is possible to develop precise prediction tools to identify individuals at risk and provide them with appropriate support before their mental health problems escalate (Allen et al., 2021).

Our project is situated within this context, focusing on the use of AI and data science to enhance early detection and management of mental health issues. By gathering and analyzing available data on family history, medical records, environmental factors, and other relevant variables, we aim to develop robust predictive models capable of identifying early signs of mental disorders and guiding individuals towards appropriate mental health resources.

Mental health is a significant and complex issue affecting thousands of individuals worldwide. The integration of machine learning has shown promise in predicting and preventing mental illnesses. This study explores the intersection of mental health and machine learning, focusing on how these advanced technological solutions can aid in predicting and diagnosing psychiatry through various machine learning algorithms. Our goal is to use this study to compare and closely examine the effectiveness of various established algorithms and enhance our understanding of the strengths and limitations of existing machine learning approaches (Livingston et al., 2020).

In our study, we have established clear objectives to improve the understanding and management of mental health issues. Firstly, our approach includes gathering relevant data on mental health from various sources, such as demographic, medical, and environmental data. This step is crucial to ensure the quality and representativeness of our analyses. Secondly, our primary objective is to develop advanced machine learning models that can accurately predict mental disorders from the collected data. To achieve this, we focus on selecting and implementing the most appropriate algorithms. Once these models are established, we will conduct a thorough evaluation of their performance, comparing them to other models as well as traditional screening methods. This will enable us to measure the effectiveness and relevance of our models in predicting mental disorders as well as their optimization. In summary, our methodological approach aims to provide a thorough understanding and more effective management of mental health issues by leveraging advances in machine learning and data science.

The integration of artificial intelligence into this project presents a significant contribution on multiple levels. Firstly, AI allows us to fully harness the power of data by enabling us to process large amounts of information efficiently and effectively. By using machine learning techniques, we can identify complex patterns and correlations within this data, enabling us to better understand the factors influencing mental health. Moreover, AI enables us to develop sophisticated prediction models that can detect early signs of mental disorders with increased accuracy. These models can be trained on historical data and continually improved as new data becomes available, allowing them to adapt to changes in mental health behaviors and trends. Furthermore, AI offers the opportunity to automate certain aspects of mental health detection and management, which can help reduce the workload of mental health professionals and improve the efficiency of care. For example, AI-powered chatbots can be used to provide immediate support and interventions to individuals in distress, thus offering rapid access to resources and guidance (Kessler et al., 2021).

In summary, the integration of AI into our project offers considerable potential to improve the understanding, early detection, and management of mental health issues by fully leveraging the benefits of data and advanced technologies.

We've identified several previous research projects that delve into predicting mental health issues. Authors in (McArdle and Ritschard, 2014), for instance, delved into machine learning, allowing the simultaneous testing of numerous factors and their complex interactions, to predict suicidal and non-suicidal self-injurious thoughts and behaviors, thereby enhancing accuracy rates.

Likewise, a few studies have aimed to identify the onset of anxiety disorders in middle-aged and older individuals using machine learning (ML). Notably, authors in (Hussain et al., 2020) utilized neuroimaging data, questionnaires, or psychometric data to train an ML algorithm to predict anxiety onset with an AUC (area under the receiver operating characteristic curve) of 0.68 ± 0.03 in the adolescent population. Their model demonstrated significant ability to anticipate future depressive episodes, paving the way for early intervention and proactive disease management.

We also scrutinize existing systems and applications that integrate features for predicting mental health issues. For instance, authors in (Chavanne et al., 2023) have developed an interface leveraging machine learning algorithms to forecast mood changes over a week. This algorithm also provides various correlations or relationships integral to this prediction.

Similarly, the Woebot application (Chavanne et al., 2023), an AI-powered chatbot employing principles of cognitive-behavioral therapy (CBT), devised by Alison Darcy, a research psychologist and technologist, has garnered attention. Woebot guides users in managing distressing thoughts and feelings. Post account creation, users interact with Woebot through messaging within the application.

While existing work in predicting mental health issues has made significant strides in research, it also presents certain limitations worth considering. Notably, the increasing use of machine learning techniques allows for a more comprehensive exploration of data and a better understanding of the complex relationships between variables associated with mental disorders. Moreover, these efforts have played a crucial role in highlighting the importance of early detection and management of mental health issues, underscoring the potential impact of early interventions on clinical outcomes. However, weaknesses such as limited sample size and generalizability of results, as well as the validity of measures used to assess mental health problems, persist. Additionally, potential biases in participant selection and result analysis can undermine the validity and reliability of conclusions. Despite these limitations, existing work has paved the way for new research avenues and underscored the need to continue efforts to develop more accurate and effective approaches for predicting and managing mental health issues.

In our approach, we've adopted a multi-step process to develop an effective prediction model. We first collected relevant data, then preprocessed it to eliminate any noise or inconsistencies. Subsequently, we selected the most significant features and trained several machine learning models to evaluate their performance.

3.1 Data Collection Process

A thorough search was conducted to identify relevant databases in the field of mental health and psychology. Several sources were explored, including academic references, online data repositories, and research archives. The database used in this project contains 26 parameters and 2100 samples, encompassing sociodemographic data, employment data, and other familial history data. Indeed, several selection criteria were applied to ensure the quality and relevance of the data for our study. These criteria included source reliability, sample representativeness, measurement quality, and availability of necessary information to address our research question.

3.2 Data Preprocessing

Once the database was selected, steps were taken to clean and preprocess the data to make it analysis-ready. This involved excluding low-quality data, duplicate data, and data irrelevant to our research question, as well as handling missing values. We then assigned default values for all data types in the "Gender" column and created lists for each type to facilitate data manipulation.

Subsequently, measures were taken to manage outliers in the age column. Values below 18 years and above 120 years were deemed implausible or potentially erroneous data. Therefore, to maintain data consistency and logic, these values were replaced with the median age in the dataset. Replacing outliers with the median was chosen as an appropriate method to mitigate the impact of these extreme values on subsequent analysis while preserving the overall age distribution in the sample. This approach helps reduce potential distortions in analysis results while maintaining data representativeness (Figures 1 and 2).

Next, we encoded categorical variables into numerical values using Label Encoding technique to make the data compatible with the algorithms to be used later (Figure 3).

Lastly, age was selected as a numerical variable and underwent scaling using the Min-Max Scaling method as it aims to standardize the ranges of variable values to make them comparable and not dominate the model (Figure 4).

3.3 Data Analysis

3.3.1 Correlation Analysis with Treatment

As part of exploratory data analysis, a correlation matrix was calculated to assess linear relationships between variables and treatment (Figure 5). The correlation matrix helps identify variables most strongly correlated with treatment, providing insights into factors influencing treatment decision. We selected the 10 variables most strongly correlated with treatment, calculated the correlation matrix, and created a heatmap to visualize correlations.

The values in the heatmap represent correlation coefficients, ranging from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no linear correlation.

3.3.2 Age Distribution and Density

To better understand the age distribution in our dataset and visualize the density of each age group, we generated a distribution and density graph. This graph depicts the distribution of ages of individuals in our dataset, illustrating the frequency of each age group and the density of the population at each age interval. This allows us to have a visual overview of age distribution among individuals (Figure 6).

3.3.3 Age Distribution and Density in the Dataset

An essential component of our study is to analyze the distribution of individuals who received treatment for mental health issues among our sample. The graph below presents the total number of individuals treated and those not treated, categorized by gender. This visualization enables us to assess the proportion of participants accessing mental health care and understand treatment disparities among different demographic groups.

3.3.4 Probability of Treatment by Age and Gender

To further examine the likelihood of mental health conditions among our samples based on their age and gender, we created a nested bar chart. This chart in Figure 7 displays the probabilities of treatment for mental health issues, divided by gender and categorized by age group. Vertical bars represent the average probability of treatment, expressed as a percentage, for each age group, while horizontal bars separated by color correspond to different genders. This visualization helps us identify general treatment trends by age and gender, offering valuable insights into mental health disparities in our study population.

3.3.5 Probability of Treatment by Family History

In this section, we present a bar chart illustrating the probabilities of mental health conditions based on family history. The chart in Figure 8 shows the probability of treatment for different categories of family history, stratified by gender. Family history categories are represented on the x-axis, while treatment probabilities are represented on the y-axis. Bars are differentiated by gender, with shades of color corresponding to each gender category. This visualization allows us to observe variations in treatment probability based on family history, as well as gender differences.

3.3.6 Probability of Treatment Based on Mental Health Benefits

In this section, we present a bar chart in Figure 9 illustrating the probability of treatment benefits related to mental health, with bars differentiated by gender. This visualization allows us to observe variations in treatment probability based on benefits, as well as gender differences.

3.3.7 Impact of Work on Mental Health and Probability of Treatment

In this section, a bar chart in Figure 10 is presented showing how work interferes with mental health and how it affects the probability of treatment, based on gender. Bars represent different ways in which work affects mental health, and the height of the bars shows the probability of treatment. This visualization helps us see how work can influence the probability of treatment for mental health and whether it varies between men and women.

3.4 Contribution’s Machine Learning Algorithms

3.4.1 Logistic Regression

Logistic regression (Wang et al., 2021) is a commonly used method for modeling binary or categorical variables, making it a suitable choice for our problem. We constructed a logistic regression model to predict the target variable from the available features in the dataset. We created and trained a logistic regression model on the training set, made class predictions for the test set, and evaluated the model's performance using various performance metrics. The results were recorded in a dictionary for later use in visualizing the performance of different models.

3.4.2 KNeighbors Classifier

The K-Neighbors algorithm (Zhao et al., 2020) is a supervised learning method used for classification and regression, classifying a data point based on the labels of its nearest neighbors in the feature space. We created and trained a K-Neighbors model on the training set. The best parameters for the model were determined using a random search. Class predictions were made for the test set, and the model's performance was evaluated using various performance metrics.

3.4.3 Decision Tree Classifier

Decision trees (Feng et al., 2020) are supervised learning models used for classification and regression, partitioning the feature space into homogeneous subsets based on feature values. We built a decision tree classification model to predict the target variable from the available features. The best parameters for the model were determined using a random search. Class predictions were made for the test set, and the model's performance was evaluated using various performance metrics.

3.4.4 Random Forest

Random forest (Rahman et al., 2020) is a supervised learning method that combines multiple decision trees to improve prediction accuracy and robustness. We used the random forest method to construct a mental health prediction model from the available features. First, we determined the best hyperparameters for our model using a random search over a predefined parameter grid. We then built our random forest model using these optimized parameters with the RandomForestClassifier class from the scikit-learn library. The model was trained on the training set and evaluated on the test set to assess its performance.

3.4.5 Bagging

Bagging (Bootstrap Aggregating) (Jemili et al., 2023) is an ensemble method that combines multiple learning models to produce more robust and accurate predictions. We used the bagging technique to construct a mental health prediction model from the available features. A base model was created using a decision tree, which was then used to form each predictor in our bagging ensemble. Using the BaggingClassifier class from the scikit-learn library, we built our bagging ensemble by specifying the base model and other parameters such as the maximum number of samples and features. The model was trained on the training set and evaluated on the test set.

3.4.6 Boosting

Boosting (Jemili et al., 2023) is an ensemble technique that combines multiple weak models to produce a more robust and accurate model. We started by creating a base model using a shallow decision tree. This base model was used to form each predictor in our boosting ensemble. Using the AdaBoostClassifier class from the scikit-learn library, we built our boosting ensemble by specifying the base model and parameters such as the maximum number of estimators. The model was trained on the training set and evaluated on the test set.

3.4.7 Stacking

Stacking (Kamel et al., 2022) involves applying a machine learning algorithm to classifiers generated by another machine learning algorithm. This approach aggregates different models to improve the final prediction quality. We used the stacking method to construct a mental health prediction model from the available features. Several base models were created using different learning algorithms, including K-Nearest Neighbors, random forest, and Gaussian Naive Bayes. Using the StackingClassifier class from the scikit-learn library, we built our stacking model by specifying the base models and a meta-classifier to aggregate their predictions. The model was trained on the training set and evaluated on the test set.

3.4.8 Neural Network

A neural network (Bahri et al., 2023) is a mathematical model inspired by the human brain, composed of multiple interconnected layers of neurons. It is used in artificial intelligence to learn from data and solve various problems, such as classification, regression, and pattern recognition. We started by creating an Adagrad optimizer instance using the Adagrad class from the TensorFlow.keras.optimizers library. We then built our neural network using the DNNClassifier class from the TensorFlow.estimator library, featuring two hidden layers with ten nodes each and using the Adagrad optimizer. The features used by the model were specified in the 'feature columns' list. This configuration ensured the model could learn complex relationships between input features and the target variable while optimizing network weights using the Adagrad optimization algorithm. Finally, we evaluated the neural network model's performance on the test set to determine its accuracy in predicting individuals' mental health.

3.5 Performance Evaluation Methods

In evaluating the performance of the aforementioned algorithms, we adopted a systematic approach to measure the effectiveness of each model in predicting mental health outcomes. First, we split our data into training and test sets using the "train test split" function, which allows us to reserve a portion of the data for evaluating the models on unseen data (see Figure 11).

Next, we used various performance metrics specific to each method to assess the predictive capabilities of the models. For classification models such as logistic regression, k-nearest neighbors, decision trees, and random forests, we used metrics including:

Accuracy: Measures the overall correctness of the model's predictions.
Precision: Indicates the proportion of correctly identified positive cases out of all cases predicted as positive.
Recall: Also known as sensitivity, measures the model's ability to correctly identify all actual positive cases.
F1-Score: Combines precision and recall into a single metric to provide a balanced measure of the model's performance, particularly useful when there is an uneven class distribution.

For neural network models, we also used accuracy as a primary performance metric, but we additionally examined metrics such as loss and the ROC curve to evaluate the model's ability to distinguish between different classes.

Finally, we compared the performances of the different methods by analyzing the results and identifying the method that demonstrates the best performance in terms of accuracy and generalization capability. This evaluation allowed us to determine which model is most appropriate for our specific task of predicting mental health outcomes, providing valuable insights for making informed decisions in our analysis.

4.1 Experimentation Results

The results of this study revealed varying performances among different classification models used to predict self-inflicted thoughts and behaviors (Fig. 12).

The performance evaluation of the models was conducted using various metrics in Table 1, including accuracy, classification error, false positive rate, and precision. These metrics quantified each model's ability to correctly predict self-inflicted thoughts and behaviors. Additionally, further analyses, such as ROC curves and confusion matrices, were used for a more detailed evaluation of model performance.

The size and composition of the dataset also played a crucial role in the models' performance. Larger and more diverse datasets tend to provide more reliable and generalizable results. Moreover, appropriate feature selection and hyperparameter tuning significantly impacted model performance. Table 1 presents a summary of the different results obtained.

In conclusion, the results of this study highlight the variable efficacy of different classification models in predicting self-inflicted thoughts and behaviors. These findings provide valuable insights for guiding model selection and optimization in the mental health domain.

Table 1

Summary of the Results of Different Classification Models Used
Model	Accuracy (%)	Classification Error (%)	False Positive Rate (%)	Precision (%)
Logistic Regression	89,61	10,39	5,65	86,44
KNN	89,82	10,18	9,31	84,89
Decision Tree	90,65	6,35	1,93	84,15
Random Forest	97,40	2,60	1,60	91,60
Bagging	87,50	12,50	4,60	86
Boosting	85,00	15,00	8,27	86,10
Stacking	86,20	13,8	2,94	90,00
Neural Networks	97,00	3,00	1,00	93,00

4.2 Discussion

4.2.1 Interpretation of the results

Analyzing the results of the various classification models reveals varying but overall encouraging performances. Starting with logistic regression, while it achieved a respectable precision of 86,44%, it also demonstrated an accuracy of 89,61%, indicating its ability to generalize well on test data. However, it also shows a classification error of 10,39%, highlighting the need to improve model precision to reduce false positives and false negatives.

Regarding the K-NN classifier, although it achieved a slightly higher accuracy of 89,82%, its classification error remains high at 10,18%. Despite this, its AUC score of 79.99% and cross-validation AUC of 87.85% indicate a moderate ability to distinguish between classes.

The Decision Tree model, with an accuracy of 90.65% and a precision of 84,15%, also ranks among the top models. However, its error rate of 6,35% indicates there is still room for improvement to reduce classification errors.

Random Forests emerged as one of the most effective models, with an AUC score of 83.45% and a cross-validation AUC of 88.03%, suggesting an excellent ability to generalize to new data. However, despite its high accuracy of 97.40%, its classification error rate remains at 2,60%, indicating the need to further enhance model precision.

Boosting, although achieving an accuracy of 85,00% and a precision of 86,10%, shows a classification error rate similar to Random Forests, highlighting the ongoing challenges associated with reducing classification errors.

The stacking technique stands out with an accuracy of 86.20% and a precision of 90%, showing significant potential for improving predictive performance by combining multiple models. Finally, the neural network, with an accuracy of 97%, demonstrates competitive performance compared to other models, underscoring its effectiveness in data classification.

To provide a clear and concise visualization of each method's success rates and quickly compare their performances, we employed a comparative analysis to identify the most promising and effective methods for our study, guiding our recommendations and final conclusions (see Fig. 13).

4.2.2 Critical Analysis of Methodology

A critical analysis of the methodology used in this study highlights several aspects requiring particular attention. Firstly, it is important to note that the methodology relies on the appropriate selection of classification models, which is essential for obtaining reliable and meaningful results. However, it is crucial to recognize that the choice of models can influence the results and introduce a certain level of bias.

Additionally, although the study performed cross-validation to evaluate model performance, it is necessary to consider the specific parameters used for this validation. For example, how the data were divided into training and test sets can impact the final results. Moreover, the cross-validation strategy used may have its limitations, especially concerning dataset size and result stability.

Furthermore, it is important to note that data quality and preparation are critical elements of any predictive analysis. Although the methodology included data preprocessing steps such as imputing missing values and normalizing features, it is important to ensure that these steps were performed appropriately and robustly to avoid potential bias in the results.

Finally, it is crucial to recognize the inherent limitations of using machine learning techniques for mental health prediction. While these approaches can provide valuable insights, they do not necessarily replace thorough clinical evaluations conducted by qualified mental health professionals. Therefore, it is essential to consider these results as complementary to traditional clinical assessments rather than substitutes.

4.2.3 Comparison with Previous Projects

Comparison with previous work is crucial to contextualize this study's results. This comparison in Table 2 can focus on several aspects, including the classification models used, selected features, evaluated performance metrics, and dataset size and composition.

Firstly, the study by (McArdle and Ritschard, 2014) explored the use of machine learning techniques to address the limitations of traditional statistical approaches in psychology. They highlighted how traditional methods restrict predictive accuracy by limiting the number of predictors examined simultaneously and imposing linearity on relationships. In contrast, machine learning approaches allow testing many factors and their complex interactions simultaneously, permitting non-linearity in predictive model creation. These researchers used several classification models, including logistic regression, decision trees, random forests, and SVMs, to predict mental health outcomes. They selected a wide range of potential features, including psychological measures, demographic data, and environmental variables. Model performance was evaluated using metrics such as accuracy, sensitivity, specificity, and the ROC curve area. The dataset used was medium-sized, composed of individuals from diverse socio-economic and cultural backgrounds. In their study, logistic regression achieved an accuracy of 79.63%, while KNeighbors and decision trees obtained respective accuracies of 79.89% and 80.69%. Random forests provided an accuracy of 83.45%. Bagging and boosting achieved accuracies of 77.51% and 81.75%, respectively, while stacking showed an accuracy of 82.28%.

In (McArdle and Ritschard, 2014) study, the researchers used several classification models, including logistic regression, decision trees, random forests, and SVMs, to predict mental health outcomes. They selected a wide range of potential features, including psychological measures, demographic data, and environmental variables. Model performance was evaluated using metrics such as accuracy, sensitivity, specificity, and the ROC curve area. The dataset used was medium-sized, composed of individuals from diverse socio-economic and cultural backgrounds.

In the study by (Chavanne et al., 2023), the researchers used neuroimaging data and psychometric scales to predict the onset of anxiety disorders in adolescents. They primarily used logistic regression methods and support vector machine-based models. Selected features included measures of brain structure obtained through neuroimaging, as well as psychological factors such as neuroticism and family events. Model performance was evaluated in terms of sensitivity, specificity, and accuracy, as well as through cross-validation. The dataset used was relatively small due to the costs associated with neuroimaging data collection but included detailed information on family history and psychometric measures. In their study, the accuracies of logistic regression and SVM models were 77% and 80%, respectively.

Comparatively, in our project, we used a variety of classification models, including logistic regression, KNeighbors classifiers, decision trees, random forests, bagging, boosting, stacking, and neural networks. The selected features were specifically related to self-inflicted thoughts and behaviors, such as age, gender, family history, and care options. Model performance was evaluated using standard measures such as accuracy, classification error, false positive rate, precision, and the ROC curve area. In our study, the accuracies of the different models ranged between 85.00% and 97.40%, with the neural network model achieving an accuracy of 97%.

These performances are comparable to those of previous studies. Thus, we can conclude that our approach is also effective in predicting self-inflicted thoughts and behaviors. Table 2 presents a comparison between the results obtained during this year-end project and those of state-of-the-art methods.

Table 2

Comparison of Classification Methods
Study	Classification Method	Accuracy (%)
(McArdle and Ritschard, 2014)	Logistic regression	79,63
	KNeighbors	79,89
	Decision Tree	80,69
	Random Forest	83,45
	Bagging	77,51
	Boosting	81,75
	Stacking	82,28
(Chavanne et al., 2023)	Logistic Regression	77,00
(Chavanne et al., 2023)	SVM (Support Vector Machines)	80,00
Our Study	Logistic regression	89,61
	KNeighbors	89,82
	Decision Tree	90,65
	Random Forest	97,40
	Bagging	87,50
	Boosting	85,00
	Stacking	86,20
	Neural Network	97,00

4.2.4 Identification of Study Limitations

Despite the promising results obtained in our study, several limitations need to be considered. Firstly, the dataset size could be considered relatively modest, which may limit the generalizability of the results to larger populations. Additionally, although we used various modeling techniques, it is possible that other unexplored methods could offer better predictive performances.

Furthermore, the dataset used may present potential biases due to self-reporting by participants, which could affect the quality and reliability of the collected data. Moreover, important features for predicting self-inflicted thoughts and behaviors might not have been included in our model due to data availability constraints. Finally, as with any observational study, there is a risk of unmeasured confounding variables influencing the results. These limitations highlight the need for a cautious approach in interpreting the results and suggest directions for future research aimed at addressing these gaps.

This study has highlighted the variable effectiveness of different classification models in predicting self-inflicted thoughts and behaviors. The results revealed that some models, such as logistic regression and stacking, produced accurate predictions, while others showed less satisfactory performance. This variation underscores the importance of carefully selecting the most suitable model for each specific case. For instance, logistic regression demonstrated a strong ability to capture linear relationships between features, while stacking combined the strengths of multiple models to enhance predictions.

However, it is crucial to note that these results are based on specific datasets and may vary depending on the size and composition of the data. Therefore, to obtain more generalizable results, further research using larger and more diverse datasets is essential. Future work could also explore the use of more advanced data preprocessing and modeling techniques, such as deep learning, to further improve prediction accuracy.

Ultimately, this study significantly contributes to the understanding of mental health disorders and paves the way for new research avenues to better predict and prevent these critical conditions.

5.1 Practical Implications and Future Directions

One potential development stemming from this study is the creation of a specialized chatbot for mental health support in the workplace. This chatbot could be designed to offer personalized support and advice to employees facing mental health issues. By utilizing machine learning, deep learning, and natural language processing algorithms, the chatbot could be trained to recognize signs of mental distress and provide appropriate resources, such as self-help tips, information on available support services, or suggestions for stress management and work-life balance. This approach could help democratize access to mental health services and provide continuous support to employees, thereby promoting mental well-being in the workplace.

Funding Declaration

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

Early Detection of Mental Health Issues through Machine Learning: A Comparative Analysis of Predictive Models

Status:

Version 1

Abstract

Figures

1. Introduction

2. Related Work

3. Proposed Approach

4. Experimentation and Discussion

4.1 Experimentation Results

4.2 Discussion

4.2.1 Interpretation of the results

4.2.2 Critical Analysis of Methodology

4.2.3 Comparison with Previous Projects

4.2.4 Identification of Study Limitations

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1