Vital Symptoms Monitoring and Classification of Bipolar Disorder
M. Kiruthiga Devi1, R. Kaviya2
1M. Kiruthiga Devi, Assistant Professor, Department of Computer Science and Engineering, Sri Sai Ram Engineering College, West Tambaram, Chennai (Tamil Nadu), India.
2R. Kaviya, UG Student, Department of Computer Science and Engineering, Sri Sai Ram Engineering College, West Tambaram, Chennai (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 07 December 2019 | Manuscript Published on 14 December 2019 | PP: 137-140 | Volume-9 Issue-1S October 2019 | Retrieval Number: A10271091S19/19©BEIESP | DOI: 10.35940/ijeat.A1027.1091S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Bipolar disorder is a mental illness that puts patients into extreme states of mind known as mania and depression. These mental states are very harmful to the lives of the patients as their day to day actions are disrupted. This project aims at identifying the symptoms of the patients who have extreme moods to determine if they are bipolar using sensors and smart phones. Patients with mental illness tend to exhibit symptoms like reduced physical activity, changes in mood, drastic changes in sleep pattern, inability to cope with stress and withdrawn from socializing. These changes can be monitored using sensors and the data collected is compared with the data collected from healthy individuals. A classification algorithm is applied to the data collected to classify the symptoms and detect if the person has bipolar disorder or is just showing subtle signs of mood swings.
Keywords: Bipolar Disorder, Random Forest Classification, Neural Network, DDMLP, Machine Learning.
Scope of the Article: Classification