Predicting depression and suicidal tendencies by analyzing online activities using machine learning in android devices

Abstract

Artificial Intelligence (AI) has brought about a profound transformation in the realm of technology, with Machine Learning (ML) within AI playing a crucial role in today's healthcare systems. Advanced systems with intellectual abilities resembling those of humans are being created and utilized to carry out intricate tasks. Applications like Object recognition, classification, Optical Character Recognition (OCR), Natural Language processing (NLP), among others, have started producing magnificent results with algorithms trained on humongous data readily available these days. Keeping in view the socio-economic implications of the pandemic threat posed to the world by COVID-19, this research aims at improving the quality of life of people suffering from mild depression by timely diagnosing the symptoms using AI in android devices, especially phones. In cases of severe depression, which is highly likely to lead to suicide, valuable lives can also be saved if adequate help can be dispatched to such patients within time. This can be achieved using automatic analysis of users’ data including text messages, emails, voice calls and internet search history, among other mobile phone activities, using Text mining/ text analytics which is the process of deriving meaningful information from natural language text. Machine Learning models analyse the users’ behaviour continuously from text and voice communications and data, thereby identifying if there are any negative tendencies in the behaviour over a certain period of time, and by using this information make inferences about the mental health state of the patient and instantly request appropriate healthcare before it is too late. In this research, an android application capable of performing the aforementioned tasks in real-time has been developed and tested for various performance features with an average accuracy of 95%.