I engineered an AI-based heart attack prediction application designed to forecast cardiac events well in advance through the real-time monitoring of a patient's vital signs. The platform serves a dual purpose:

• For Doctors: It provides a remote platform to monitor their patients' vitals in real-time from anywhere in the world.

• For Users/Patients: It offers the ability to instantly perform a health check-up anywhere and anytime with a single click.

Monitored Vital Signs

The system focuses on tracking four primary cardiovascular metrics:

• Pulse Rate

• Pulse Pressure

• Arterial Pressure

• Blood Pressure

System Architecture & Workflow

My team and I architected a multi-node, cloud-centric system to process and analyze health data. The workflow is divided into four main technical components:

• Wearable Gadget: This hardware component acts as the initial data collection point, gathering sensitive health data directly from the patient and transmitting it to the cloud.

• Cloud & Database: The cloud serves as the central routing hub. It receives the incoming data from the wearable, securely stores the patient's sensitive information within the Database (DB), and forwards the necessary data points to the AI engine.

• Bayesian Net: This acts as the analytical core of the project. The Bayesian network analyzes the patient's symptoms and health analytics to generate predictive models regarding heart attack risks.

• Web App: The front-end interface where the processed data and predictions are visualized for the end-user.

User Interface (UI)

The mobile/web application mockups showcase the dark mode aesthetic I frequently favor in UI design, providing a clean, high-contrast dashboard. The interface visualizes the data through:

• A prominent "Heart Score" gauge (e.g., displaying a score of 89).

• Detailed radar/spider charts for multi-variable health analysis.

• Horizontal bar graphs that track current status metrics alongside navigation menus for different tracking modules.