44 A Comparative Analysis of Machine Learning Algorithms and Their Performance in Mental Health Conditions Shalbbya Ali 1 , Dr. Suraiya Parveen 2 1 Department of Computer Science & Engineering, School of Engineering Sciences & Technology 2 Department of Computer Science & Engineering, School of Engineering Sciences & Technology Jamia Hamdard University, New Delhi (E-mail: 1 shalbbya.ali@gmail.com, 2 husainsuraiya@gmail.com ) Abstract—Mental Health is a state in which a person is able to manage life's stressors, reach their full potential, learn and work effectively, and give back to their community. Early prediction of mental health conditions can be made possible with the help of ongoing advancements in machine learning technology, making it simple to identify the disease and offer timely intervention. In recent years, machine learning algorithms have shown encouraging results in terms of grouping and classifying mental health illnesses. Different classifiers including SVM, KNN, K-Mean clustering, neural networks, decision trees, and others, can be used to diagnose mental health conditions. The goal of this paper is to provide a comparative analysis of the various machine learning methods used in the medical field for mental health diagnosis and prediction. Keywords—machine learning, algorithms, mental health, classification I. INTRODUCTION The field of machine learning has burgeoned with remarkable growth in recent years, witnessing the development of an extensive array of algorithms tailored to address a multitude of tasks, spanning from classification to regression and clustering. The WHO estimates that 14% of those between the ages of 10 and 19 have mental health issues [1]. Adolescents may become the source of illness and impairment due to behavioral issues, anxiety, and depression [2]. Delays in identifying and taking action could lead to long-term problems like poorer academic performance, less social interaction, and even suicide. Therefore, developing a successful strategy for early detection and intervention is crucial. AI has simultaneously sparked a revolution in healthcare and medicine. AI is seen as a cutting- edge tool for monitoring and diagnosing mental health problems in both people and communities, as well as for arranging mental health therapies[14]. Digital health-care data is available in many forms, such as e-health records, medical images, and written by hand clinical observations. AI-driven solutions can use this data to automate tasks, assist physicians, and deepen our understanding of the underlying mechanisms of complex disorders. Despite the high expense of the technology used in these systems, artificial intelligence (AI) systems have the potential to reduce unnecessary variance in clinical practice, improve efficiency, and minimize preventable medical errors[15]. In the long run, these benefits may outweigh the costs. Handwritten notes, subjective, qualitative outpatient remarks, and patient interviews are common methods used to gather pre-clinical data on mental health. AI has enormous potential to transform clinical intervention and our knowledge of mental health conditions [16]. By using AI techniques, better pre-diagnosis screening instruments and risk models to determine a person's likelihood of developing mental illness can be created. The cultural context of mental health reporting differs in the Asian subcontinent from the Western subcontinent, so even though a lot of work is being done to predict mental well-being in clinical and non-clinical settings, there is still a significant need to synchronize machine learning predictions with pre- clinical mental health anxiety stages in order to deliver individually tailored mental health recommendations [17]. In response to this proliferation, this study aims to offer a comprehensive comparison of several prominent machine learning algorithms used to diagnose mental health conditions. By illuminating their individual strengths, weaknesses, and performance across diverse applications, this analysis endeavors to provide valuable insights for healthcare practitioners navigating the complex landscape of machine learning. A. Supervised Learning Algorithms Supervised learning algorithms offer diverse approaches for learning from labelled data and making predictions or decisions based on input features. Linear regression is a foundational tool for regression tasks due to its simplicity and interpretability, but it may struggle with capturing complex data patterns due to its assumption of a linear relationship between features and target variables. Logistic regression, favored for binary classification tasks, provides probabilistic interpretations but may encounter challenges with nonlinear relationships between features and targets.