Rural Depression Patients Judgment Model Based on Machine Learning

Depression is a serious mental disorder. According to data released by the World Health Organization last year, there are approximately 300 million people suffering from depression worldwide. Due to the backwardness of medical care in rural areas and the low level of understanding of depression among residents, mental illness affects individuals in rural areas more seriously than in urban areas. However, with the development of artificial intelligence technology, related techniques such as machine learning have made initial gains in the field of depression determination and treatment. In this paper, based on a survey of data from Busara Center in Kenya, various machine learning methods such as K-NN, Naive Bayes methods, Support Vector Machine Classifier and Random Forest Classifier were used and evaluated for the most accurate model. The results show that Random Forest Classifier has the highest accuracy, at 0.794.