Predicting Depression and Suicidal Tendencies by Analyzing Online Activities Using Machine Learning in Android Devices - Real-Time Android AI System

Aug 5, 2026

Artificial Intelligence and Machine Learning enable timely mental-health support by detecting depressive and suicidal risk signals from everyday digital behavior. This research focuses on people with mild depression, aiming for earlier diagnosis and improved quality of life via an android-based solution, with the potential to save lives in severe cases where suicide risk is high. The proposed approach continuously analyzes user-generated data such as text messages, emails, voice calls, and internet search history using text mining and sentiment-oriented models trained for on-device detection, achieving high reported average accuracy.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Literature Review

Proposed System and Methodology

Android Implementation

Experimental Setup and Results

Conclusion

[https://doi.org/10.22581/muet1982.2401.2175](https://doi.org/10.22581/muet1982.2401.2175) 2024, 43(1) 213-224 Predicting depression and suicidal tendencies by analyzing online activities using machine learning in android devices Sara Qadeer * , Khuhed Memon, Ghulam Hyder Palli Department of Electronic Engineering, Mehran University of Engineering and Technology Jamshoro *Corresponding author: Sara Qadeer, Email: [sara.rajput@faculty.muet.edu.pk](sara.rajput@faculty.muet.edu.pk) Received: 01 October 2023, Accepted: 20 December 2023, Published: 01 January 2024 K E Y W O R D S A B S T R A C T Natural Language Processing Sentiment Analysis Machine Learning Text-mining Depression Suicide 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% . 1. Introduction Depression is a common and very serious, yet treatable, medical illness that affects patients’ quality of life. It has a negative impact on what you think, feel or do, causing patients suffering from it to feel dismal or lose interest in everyday activities. Its causes are widespread, ranging from hereditary, along with relation to pessimistic personalities, to environmental factors, such as long-term exposure to violence, neglect, abuse or poverty. It can even affect people living in relatively ideal circumstances. In its severe state, it can even lead to suicide, which is oneof the leading causes of death in the developed countries. Identifying an individual’s state of mind can greatly assist in improved quality of life and can serve as an important means of suicide prevention in extreme cases of depression. This study aims at achieving this cause with the help of an android application designed to analyze a person’s behavior and mental condition using AI. An android phone user’s text and voice communication, along with internet search history can be analyzed with the help of machine learning by training and deploying custom TensorFlow models in an android application. The timely predictions from such intelligent systems about a suspected volatile outburst from an individual can assist in launching adequate help in order to prevent suicides in

FAQs

How does the android application detect depression and suicidal tendencies?

It analyzes users’ text, voice, and internet search history, then applies a trained machine learning model using NLP to monitor behavior continuously and infer mental-health state.

Which types of online activities are used as input for the analysis?

The system uses text messages, emails, voice calls and audio recordings, plus internet search history and other mobile phone activity data.

Why is local processing on the phone important in this research?

Processing locally helps preserve user privacy and reduces network usage, making the system more secure and cost efficient.

Aug 5, 2026

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