Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text (2024)

Abstract: Mental health analysis from social media text demands both high accuracy and interpretability for responsible healthcare applications. This paper explores Kolmogorov Arnold Networks (KANs) for mental health detection and classification, demonstrating their superior performance compared to Multi-Layer Perceptrons (MLPs) in accuracy while requiring fewer parameters. To further enhance interpretability, we leverage the Local Interpretable Model Agnostic Explanations (LIME) method to identify key features, resulting in a simplified KAN model. This allows us to derive governing equations for each class, providing a deeper understanding of the relationships between texts and mental health conditions.

## introduction mental health illness, which gained significant importance in recent years is still one of the serious health concerns worldwide @xcite .there are multiple mental illnesses from stress, depression, and anxiety to more severe ones like schizophrenia, autism spectrum disorder (asd) etc., affecting millions globally. not only does it influence an individual's health, but also has a significant impact on society's health and well-being as a whole. in 2019, it was reported by the world health organization (who) that a staggering 1 out of 8 individuals were suffering from one or the other mental disorders , the most prominent ones being anxiety and depression. 1 some studies have revealed that suicidal risk in an individual is strongly connected to his mental health condition @xcite . as per the reports published in foot_1 , a staggering 1.6 million individuals in england were waiting for mental health care services. thus, comprehending various mental disorders necessitates developing a robust and accurate classification system for early detection. there are numerous ways in which individuals express their moods, feelings, and personal life experiences, with social media platforms being the most prominent one's @xcite @xcite .these writings can be used to make inferences about one's mental state leveraging nlp techniques @xcite since around 80% of people tend to disclose their suicidal thoughts on these platforms @xcite . in particular, for applications like healthcare, an accurate and transparent decision-making process is needed because incorrect predictions might lead to life-altering consequences.despite the superior performance llm's and deep-learning methods are still treated as black-boxes due to their lack of interpretability @xcite . our work embarked on exploring the power of kolmogorov-arnold networks (kan) to enhance classification accuracy over the traditional multilayer perceptron(mlp). simultaneously, to interpret the outputs and provide transparency we employed lime (local interpretable model-agnostic explanations) as an xai approach.the novelty of our work lies in the application of kan for multiclass classification as well as for binary classification in the realm of mental health followed by using xai approach to gain insights into the explainability of our model. our work stands out as the first to combine kan with explainability techniques for mental health classification. ## related works earlier studies focused on traditional machine learning methods typically following a pipeline approach. among these methods, supervised learning is predominantly employed including svm @xcite @xcite , k-nearest neighbors (knn) @xcite logistic model tree ajay surya jampana, mohitha velagapudi, neethu mohan, sachin kumar s amrita vishwa vidyapeetham (lmt) @xcite @xcite , logistic regression @xcite and several ensemble models @xcite @xcite . reinforcement learning was also used in depression detection by giving attention to only useful information instead of noisy data @xcite . deep learning techniques have started gaining more attention for detecting mental illness in recent years. @xcite used roberta to classify 5 mental health conditions from reddit posts (anxiety, bipolar disorder, adhd, depression, and ptsd). numerous studies have focused on the analysis of language used in social media for classifying emotions @xcite or aiming to identify depressed individuals. by focusing on linguistic choices made by individuals from controlled user groups and depressed user groups the researchers were able to classify the depressed individuals @xcite @xcite . subsequently, @xcite examined the likelihood of generation of characters as a sequence by employing character-level language model. @xcite incorporated word embeddings with nn models like cnn and rnn for depression detection. in @xcite experimented with hierarchial attention networks (han) as part of a multi-class classification and designed a binary classifier for each disorder. besides their remarkable performance using deep learning techniques, they are unable to provide transparency in predictions. most of the research in mental health detection is majorly centered around improving accuracy over a model's ability to interpret as shown in @xcite . to the extent of our understanding, only a handful of papers, as noted in @xcite @xcite have focused on explainability. ## materials and methods in this section, we provide a comprehensive overview of the materials and methodologies employed in our study. we detail the preparation of datasets used for mental health detection and classification, describe the feature extraction methods, and elaborate on how kolmogorov arnold networks (kan) were trained and compared with other existing models. ## proposed framework the steps involved in the classification of mental health diseases for both datasets using the proposed method is presented as a block diagram in fig. 2 . ## results in the proposed study, a set of well-established evaluation metrics for classification, including precision, recall, f1-score, and average accuracy, were used to assess the effectiveness of the proposed framework across multiple datasets. to benchmark the performance of kolmogorov arnold networks (kan), we compared it with the support vector machine (svm) utilizing the neural tangent kernel (ntk) @xcite , as well as the multilayer perceptron (mlp). svm with ntk was chosen due to its ability to perform well in smallsample scenarios and its theoretical connections to neural networks, which offer valuable insights when compared to kan's neural structure. on the other hand, mlp is widely used in classification tasks due to its simplicity and capability to approximate any continuous function, making it a close structural competitor to kan. fig. 3 presents the receiver operating characteristic (roc) curves for the kan model, plotted for each class in the rmh dataset. these scores are indicative of the kan model's strong classification performance, with adhd achieving the highest auc of 0.98, followed by anxiety (@xmath0.96), suicidewatch (@xmath1.92), and depression (@xmath2.87). the higher auc values signify that the model is able to maintain a high true positive rate with relatively fewer false positives, which is crucial in sensitive applications such as mental health classification, where misclassification can have serious consequences. ## model interpretation after training the kan model, we employed the lime (local interpretable model-agnostic explanations) explainability technique to identify the most important features that contributed the most to computing the output. lime generates a local surrogate model, typically a linear model, that approximates the predictions of the complex model around the instance of interest @xcite . this method is particularly suitable for our problem because it allows us to interpret the model's behavior for specific predictions, making it easier to explain individual decisions, which is crucial for applications in mental health. the features that cause significant changes in the predictions are assigned higher importance scores. we fixed a threshold for the feature importance scores that selected the 5 most contributing features above this threshold as seen from table . 3 & 4. ## conclusion our study explored kolmogorov arnold networks (kan) as an interpretable alternative to traditional neural networks for mental health detection and classification from social media text. kan outperformed multilayer perceptrons (mlp) and svm with neural tangent kernel (ntk) in terms of accuracy and other evaluation metrics across the rmh and mh corpus datasets. kan's unique architecture allows for model simplification and visualization through governing equations, enhancing interpretability-an essential feature for mental health prediction tasks. by utilizing the lime explainability technique, we identified key features influencing the model's output, enabling the creation of a streamlined kan model without sacrificing performance.