Mental Health Monitoring and Detection Based on Machine Learning and IoT Data

Recently, the Internet of Things (IoT) has become a buzzword in various technology fields because of its many applications. Healthcare is one of the most important fields in daily life and holds significant interest for IoT and artificial intelligence researchers. In the area of healthcare, the mental healthcare field has attracted many researchers and funding organizations for various reasons. Among those reasons are the adverse impact of mental conditions on both individuals and society, the high costs of mental care, the nature of mental conditions and their hidden and unclear symptoms, and the stigma associated with them. In this work, an approach to building an IoT mental health monitoring and detection system has been proposed. The depression problem is used as a case study for the mental conditions. The proposed approach involves data collection, data pre-processing, feature extraction, feature selection, and model building. Machine learning (ML) and data mining techniques, along with different data types for training and testing the models, have been utilized to accomplish the tasks of the proposed approach. Four ML algorithms have been investigated to detect and predict the diagnosis of the mental state. These algorithms are random forest, decision tree, support vector machine (SVM) and k-nearest neighbors (KNN). Our ML detection model has achieved a high detection accuracy of 95% using the random forest algorithm. Comparisons in different aspects with other works are presented. Our work has the advantages of using ML, adopting different data types, and therefore achieving better classification.