PSYCHIATRIC MENTAL DISORDER PREDICTION USING ARTIFICIAL NEURAL NETWORK

Abstract

Psychiatric mental illnesses represent a serious public health risk, and successful management of these conditions depends on early detection and intervention. The use of artificial neural networks (ANN) as a predictive tool to identify people who may acquire psychiatric mental problems is examined in this research. Artificial neural networks (ANNs) are capable of efficiently learning patterns and associations that human clinicians might not be able to see by utilizing vast databases of clinical and demographic data. In Ethiopia, mental disorders are the most leading non communicable disorder [8]. According to World Health Organization (WHO) report shows that 4,480,113 (4.7%) and 3,139,003 (3.3%) people in Ethiopia are estimated to suffer from depression and anxiety respectively; the total years lived with a disability was about 837,683 (10.1%) led by depressive disorder and 292,650 (3.6%) by anxiety disorder. Experimental research design was used for predictions of 8 classes of target variables by using 30 independent variables. This research paper used ANN model with three different architectures. MLP_1L, MLP_2L, MLP_3L were trained with different hyperparameter values and achieved 98.9%, 99.5%, 99.5 respectively. We used MLP_3L for prediction of each disorder types and we achieved accuracy of Bipolar = 99%, ADHD= 97.5%, PTSD = 99.4%, Anxiety = 99.5%, Major depressive disorder = 99.5%, OCD = 99.9%, Schizophrenia = 99.9%, and PD = 99.4% respectively. The result we have in our experiments have proven that MLP_2L and MLP_3L model can significantly support accurate prediction of psychiatric mental disorders with highest accuracy than MLP_1L