Abstract- Childhood mental disorders such as anxiety, depression and attention deficit disorder are commonly found amongst children. It is crucial to diagnose these problems at an early stage to ensure proper treatment and to prevent further complications. Machine learning techniques can be applied to analyze a patient's history, aiding in the diagnosis of the problem. In this research, three machine learning techniques have been identified and compared based on their performance in accurately diagnosing five common mental health disorders. The objective is to determine the most accurate technique. The dataset contains sixty attributes, but only twenty-five attributes were found to be important in diagnosing the disorders. By ignoring irrelevant attributes, the techniques were evaluated based on their performance on selected attributes.

| DOI: 10.17148/IARJSET.2023.10844