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A Machine Learning Based Study to Predict Depression with Monitoring Actigraph Watch Data | IEEE Conference Publication | IEEE Xplore

A Machine Learning Based Study to Predict Depression with Monitoring Actigraph Watch Data


Abstract:

The consequences of depression are breathtaking these days. The suicidal tendency, as well as other fatigues, depression has almost soaked the world. A detection system c...Show More

Abstract:

The consequences of depression are breathtaking these days. The suicidal tendency, as well as other fatigues, depression has almost soaked the world. A detection system can combat such consequences early. Motor activity sensor values carry out an individual's daily routine activities that can somewhat signify momentary changes in behavior. A consolidation of these motor sensor data with other demographic, clinical data can be very convenient in terms of depression detection. The combination of motor sensor reads as well as demographic data has been obligated in this study with machine learning approaches, namely Random Forest(RF), AdaBoost, and Artificial Neural Networks (ANN), achieving accuracy and Fl-score of 98% in both cases. The Cohen's kappa coefficient and Matthew's correlation coefficient are 0.96 in both factors.
Date of Conference: 06-08 July 2021
Date Added to IEEE Xplore: 03 November 2021
ISBN Information:
Conference Location: Kharagpur, India

References

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