Examination of Machine Learning Algorithms in Diagnosis of Neurological and Mental Diseases

Mental health conditions, including anxiety, represent major challenges on a global scale. These illnesses encompass a range of conditions that disrupt thought patterns and behavior, often leading to significant discomfort or disability for those affected. the field of data mining has gained prominence in medicine, offering innovative tools to uncover hidden insights and enhance disease classification, particularly in mental health. This analytical method is essential for uncovering valuable patterns in large datasets, enabling better understanding and diagnosis of complex disorders. The purpose of this article is to investigate neurological and mental diseases using machine learning algorithms and to identify the most used algorithm in each disease. The method used in this article is machine learning algorithms and it is the most widely used and most important algorithm in each of the neurological and mental diseases. The results show that the SVM algorithm emerged as the most frequently employed method, followed closely by random forest and decision tree algorithms. These techniques demonstrate the growing importance of machine learning in enhancing diagnostic capabilities and deepening our understanding of mental health disorders. This research focuses on utilizing machine learning techniques to assist in diagnosing neurological and mental health conditions. By analyzing studies conducted between 2005 and 2024, the review evaluates conditions such as schizophrenia, depression, bipolar disorder and Alzheimer. A total of 50 studies were selected based on their relevance to machine learning applications in this domain.