Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder

Abstract

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposed Weighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches.

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

“…We can develop customized sensor arrays that can make wearability easy and less obvious. We can also implement ML to the system to make data processing faster and more accurate [129][130][131]. We can also implement blockchain to bring a layer of security needed in the system to perform to its capabilities [132,133].…”

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

“…An Ensemble Learning algorithm was therefore applied to improve the predictive performance of the final model [ 24 ]. The application of Ensemble Learning averaged the different hypothesis predicted from the previously applied algorithms and reduced the risk of selecting an incorrect hypothesis [ 25 ]. The weighted average ensemble model shown in Equation ( 4 ) combines outcomes from the Decision Tree, Random Forest and XGBoost models.…”

Coresets for the Average Case Error for Finite Query Sets

“…Theoretically speaking, the 1-mean problem (also known as the arithmetic mean problem), is a widely used tool for reporting central tendencies in the field of statistics, as it is also used in machine learning. As for the practical aspect of such problem, it can be either used to obtain an estimation of the mathematical expectation of signal strength in a area [ 50 ], or as an imputation technique used to fill in missing values, e.g., in the context of filling in missing values of heart monitor sensor data [ 51 ]. Note that a variant of this problem is widely used in the context of deep learning, namely, the moving averages.…”