Research Article
Detection of Psychological Stability Status Using Machine Learning Algorithms
@INPROCEEDINGS{10.1007/978-3-031-35078-8_5, author={Manoranjan Dash and M. Narayana and Nampelly Pavan Kalyan and Md Azam Pasha and D. Chandraprakash}, title={Detection of Psychological Stability Status Using Machine Learning Algorithms}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Machine Learning Logistic Regression Random Forest Deep Neural Network}, doi={10.1007/978-3-031-35078-8_5} }- Manoranjan Dash
M. Narayana
Nampelly Pavan Kalyan
Md Azam Pasha
D. Chandraprakash
Year: 2023
Detection of Psychological Stability Status Using Machine Learning Algorithms
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_5
Abstract
Obviously, individuals all over the world make a solid effort to stay aware of the hustling scene. Nonetheless, thus, every man and lady is managing interesting wellness issues, one of the most notable of which is misery or stress, which can prompt passing or other horrifying demonstrations. These inconsistencies are alluded to as bipolar problem, which can be treated by following a couple of expert suggested medicines. Victims who have been determined to have psychological wellness issues have their circumstances analyzed to assist them with approaching their regular routines. Positive conditions, such as Schizophrenia and Bipolar Disorder, have a higher likelihood of continuing crises. Mental health professionals are responsible for reducing the risk of patients experiencing crises. Machine learning is being used by neuroscientists and therapists all around the world to widen treatment regimens for patients and to identify some of the key signs for mental health issues before they manifest. One of the benefits is that device learning helps practitioners to predict who might be at risk of a specific condition. For this study, statistics were gathered from working humans, and the dataset was ran through a few machine mastering algorithms, which included all forms of queries for depressed identification. When compared to DNN and Logistic Regression, the Random Forest algorithm delivers the best accuracy of 81.02% after applying a few algorithms to the data set.
