Assessing psychological risks severity level using variants of k-nearest neighbor algorithm
Amita Banerjee, Sushruta Mishra, Vinita Malik, Victor Hugo C. de Albuquerque
Abstract
The world is now a field for perpetual rat race in every sphere of life and it has grown to be a space where about one thirds of the population suffers from anxiety disorder, proving it to be the most prevalent mental disorder. Keeping all concerns and the greatly developing technologies in mind, this study uses an intelligent analytics method for the responses of the DASS 21 Questionnaire, to train a K-Nearest Neighbour(KNN) model to predict psychological disorders and works on increasing the accuracy of the model. Machine learning models are of immense significance to numerous clinical issues as it may be helpful in diagnosing an illness, to analyze its symptoms and to forecast the result of a diagnosis. K-Nearest Neighbors algorithm (KNN) is found to generate the best performance in predictive analysis of this study. Different models of KNN like 1-NN, 3-NN, 5-NN and 7-NN are examined for the model among which 5-NN was found to be the best. Average accuracy, precision, recall and f-score noted with 5-NN were 89.6, 91.3, 0.89, 0.8975 respectively. Also a latency of 4.4 seconds was found with 5-NN. In the process, Emotional process, linguistic style, temporal process were also utilized for different KNN techniques to improve the results and predictions of the model.