Aditi Singh
Vol. 17, Jan-Jun 2024
Abstract:
People now communicate on a variety of internet channels on a daily basis. Natural language processing techniques can be used to deduce users' mental states based on textual or spoken information they post on these sites. Using SMS to predict mental health issues is a proactive step toward better treatment. NLP is transforming the way that professionals in the field of mental health assess patients' freedom of expression in order to identify and diagnose mental illnesses. In addition to offering new avenues for research into human attitudes and behaviors, machine learning techniques can be used to recognize the telltale indications and symptoms of mental illness. In this study, we investigate various supervised classifier methods in depth and use natural language processing (NLP) to identify the mental health state from a text message. People experience suffering from several mental diseases, but the most common ones include PTSD, bipolar disorder, panic disorder, depression, stress, and anxiety. We used Decision Trees, Random Forest, K-Nearest Neighbors, BernoulliNB, and Logistic Regression to classify the data for this investigation. In comparison to the other four classifiers, Logistic Regression performs the best in our suggested strategy. The experimental result confirms that more accurate patient data classification can be achieved with the suggested methodology. With a 93 percent accuracy rate, the suggested model was demonstrated to be efficient.
DOI: http://doi.org/10.37648/ijrmst.v17i01.009