Al-Powered Predictive Models for Personalized Mental Health Care
Pamulapati Taruna Sri Department of Computer Science and Engineering,
Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India 2200030181@kluniversity.in
Majji Prasad
Department of Computer Science and Engineering,
Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India 2200032853@kluniversity.in
Kalyanam Abhishek Department of Computer Science and
Engineering, Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India 2200030867@kluniversity.in
Vinnakota Pooja Sri Department of Computer Science and
Engineering, Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India 2000032090@kluniversity.in
Ms. Gnana Deepthi Bitra Department of Computer Science and
Engineering, Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India bndeepthi@kluniversity.in
Abstract— This project is about building an AI-powered platform that helps people get personalized mental health care. It’s designed to solve big problems like delays in getting help, one-size-fits-all treatments, and poor early detection of mental health issues [1]. The platform uses wearable devices and mobile apps to track things like heart rate (HRV), sleep, and activity levels in real time. This data is then used to train machine learning models using TensorFlow [3, 5].
These models help spot people who might be at risk and suggest customized support like CBT (Cognitive Behavioral Therapy) exercises, mindfulness sessions, or even referrals to mental health professionals [10]. The platform follows strong privacy and ethical standards (like HIPAA and GDPR), and meets international quality standards like ISO 13485 and ISO 9001 [7].
Important features include secure systems for handling data, dashboards that mental health experts can use to monitor patients, and a flexible setup that makes it easy to scale globally [9]. Inspired by new digital health technologies, this project wants to make mental health care easier to access—especially in areas where help is hard to find—and give both patients and providers better tools to work together [1, 15].
Keywords— Artificial Intelligence, Machine Learning, Wearable Devices, Mental Health, Personalized Care, Predictive Analytics, Digital Phenotyping, Mobile Applications, Stakeholder Dashboards, Data Privacy, Ethical AI, Scalability, Compliance Standards, Real-Time Monitoring, Intervention Delivery
