Objectives
. Designing an AI driven diagnostic system that enhances accuracy and reduces diagnostic time. . Ensuring affordability and accessibility of AI powered healthcare tools in remote and underprivileged areas .Developing a scalable solution that integrates with existing healthcare frameworks and telemedicine services.
Socio-Economic Benefit
.Reduced healthcare costs .Improved productivity .Lower burden on healthcare systems . Cost savings for insurance companies . Increased workforce participation
Methodologies
The methodology for smart healthcare disease prediction involves integrating data science, medical knowledge, and real-time analytics to identify potential diseases before they fully develop. Below is a structured overview of a typical methodology used in such systems: . Data Collection . Data Preprocessing . Machine Learning Model Development . Model Training & Validation .Interpretability & Explainability . Deployment . Feedback Loop & Continuous Learning . Privacy, Ethics & Security
Outcome
. Improved Disease Detection Accuracy . Reduction in Healthcare Costs . Enhanced Patient Outcomes . Scalable AI/ML-Based Prediction Models . Real-Time Risk Stratification . Data-Driven Decision Support for Clinicians . Privacy-Compliant and Secure System
Project Team Members
| Registration# | Name |
|---|---|
| CU-2111-2021 | Ali |
| CU-2108-2021 | Huzaifa |
| CU-2472-2021 | Muhammad Waqas |
PROJECT GALLERY
Smart Health Care Disease Prediction
27 May, 2025 10:04 am
Smart Health Care Disease Prediction
27 May, 2025 10:04 am
