DIAGNOSING EMOTIONAL ABUSE AMONG PRIMARY SCHOOL STUDENTS USING MACHINE LEARNING TECHNIQUES

The child\'s mental and physical health plays an important role in the development of society, as it constitutes the cornerstone in the development and upbringing of society. Therefore, most developed and underdeveloped countries seek to give the child priority in their plans and strategies. One of the risks for the sponsor is emotional abuse. Which requires the use of methods and strategies to detect these risks. Some machine learning methods provide the ability to classify and therefore we tried through our research to use them as a method for diagnosing the child\'s mood in terms of emotional abuse. This research examines the ability of machine learning algorithms to diagnose emotional abuse among children in Iraq. The study concluded that the use of each of the previous methods can diagnose emotional abuse in a child, but the nearest neighbor algorithm is considered the best method for diagnosing emotional abuse, according to the evaluation criteria used.