MENTAL HEALTH MONITORING USING ARTIFICIAL INTELLIGENCE

Mental Health disorders have become one of the significant public health concerns across the world, requiring proper and prompt diagnostic tools. These stresses of everyday life breed a load of terminology including stress, anxiety, and mood swings. These feelings may blossom to depression and more complex mental problems. Predictions about the type of mental disorder using artificial intelligence, namely the Random Forest Algorithm, which has gained wide recognition for its effectiveness in classification problems. The primary purpose of 'mental health prediction' is to predict the mental health of the patient based on symptoms and diagnose the exact disease in order to resolve the serious issues related to mental health, which are ignored by the society while considering distraught mental health a taboo. This paper makes a survey of various mental health symptoms and problems related to it in our society that are being solved using AI technologies. For testing the performance of our proposed system, we used several machine learning algorithms like Support Vector Machines (SVMs), Random Forest (RF) Algorithms, and K-NN classifier. Here, these Algorithms are used mainly for the diagnosis of mental health disorders based on the given input; i.e., the verified dataset of symptoms. The Random Forest Model achieved an overall accuracy of 95% for predicting the type of the mental disorder. Gain in the values of Precision, Recall, and F1 – Score was also noted.