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A Comprehensive Healthcare System to Detect Depression


Affiliations
1 SRM University, 100 Feet Road, Vadapalani, Chennai - 600026, Tamil Nadu, India
 

Objectives: To develop a home-based healthcare unit that uses smart phones to monitor depression statistics. Methods: To tackle depression, we propose a mechanism which systematically monitors the user’s behavior on a daily basis. We make use of a Smartphone to obtain GPS and phone usage data from the user and employ a clinically approved survey (PHQ-9) to monitor their mental state. The result obtained from computation determines the level of depression of the given user which is then sent to the consulting physician. Findings: Depression is one of the major psychological issues present in today’s world, which often goes unnoticed because of its subtle symptoms and social stigma. Also, that people are too engrossed within their daily duties does not help them realize the problem even when depression related symptoms prevail. A study based on the World Health Organization’s World Mental Health Survey Initiative has said that India has the highest rate of major depression in the world. An untimely detection of depression related symptoms may prove to be detrimental to several vital parameters of the body. By the proposed implementation, the physician can conveniently monitor the user’s health status periodically without spending much time explicitly being a part of the process. The application of this healthcare system is to detect the onset of depression and prevent drastic results such a suicide and help the patient be in contact with their physician at all times. Improvements: Future improvements include, making the whole process less invasive by implementing Medical Imaging (Brain Scans) and incorporating IoT to increase parameters in the decision making matrix of depression detection.

Keywords

Cluster, DBSCAN, PHQ-9, GPS, Phone Usage.
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  • A Comprehensive Healthcare System to Detect Depression

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Authors

M. Poonkodi
SRM University, 100 Feet Road, Vadapalani, Chennai - 600026, Tamil Nadu, India
Ananya Srinivasan
SRM University, 100 Feet Road, Vadapalani, Chennai - 600026, Tamil Nadu, India
Bhavana Tumma
SRM University, 100 Feet Road, Vadapalani, Chennai - 600026, Tamil Nadu, India
Srividya Ramaswamy
SRM University, 100 Feet Road, Vadapalani, Chennai - 600026, Tamil Nadu, India

Abstract


Objectives: To develop a home-based healthcare unit that uses smart phones to monitor depression statistics. Methods: To tackle depression, we propose a mechanism which systematically monitors the user’s behavior on a daily basis. We make use of a Smartphone to obtain GPS and phone usage data from the user and employ a clinically approved survey (PHQ-9) to monitor their mental state. The result obtained from computation determines the level of depression of the given user which is then sent to the consulting physician. Findings: Depression is one of the major psychological issues present in today’s world, which often goes unnoticed because of its subtle symptoms and social stigma. Also, that people are too engrossed within their daily duties does not help them realize the problem even when depression related symptoms prevail. A study based on the World Health Organization’s World Mental Health Survey Initiative has said that India has the highest rate of major depression in the world. An untimely detection of depression related symptoms may prove to be detrimental to several vital parameters of the body. By the proposed implementation, the physician can conveniently monitor the user’s health status periodically without spending much time explicitly being a part of the process. The application of this healthcare system is to detect the onset of depression and prevent drastic results such a suicide and help the patient be in contact with their physician at all times. Improvements: Future improvements include, making the whole process less invasive by implementing Medical Imaging (Brain Scans) and incorporating IoT to increase parameters in the decision making matrix of depression detection.

Keywords


Cluster, DBSCAN, PHQ-9, GPS, Phone Usage.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i47%2F133451