Mockup Description

i need system architecture and mock ups for this 1. Title of the Study Design and Implementation of a Web-Based AI System for Early Cardiovascular Risk Stratification and Appointment Prioritization 2. Background of the Study Cardiovascular diseases (CVDs) are among the leading causes of death globally and account for a significant number of hospital admissions each year. Early detection of high-risk patients is critical in preventing severe complications such as heart attacks and strokes. However, many healthcare facilities continue to rely on traditional appointment systems that operate on a first-come, first-served basis rather than prioritizing patients based on medical urgency. With the increasing availability of electronic health records (EHRs) and structured medical datasets, Artificial Intelligence (AI), particularly machine learning, provides an opportunity to analyze patient data and classify cardiovascular risk levels. Integrating AI into a web-based platform can enable efficient patient risk stratification and intelligent appointment prioritization, thereby improving healthcare delivery and patient outcomes. 3. Problem Statement Healthcare institutions often struggle to efficiently prioritize cardiovascular patients due to increasing patient numbers and limited specialist availability. Current appointment scheduling systems do not incorporate predictive risk analysis, which may result in delayed care for high-risk patients. Although hospitals collect valuable patient data such as blood pressure, cholesterol levels, and ECG results, this information is not fully utilized for automated risk assessment and scheduling decisions. There is therefore a need for a web-based AI-driven system that can analyze cardiovascular patient data, classify risk levels accurately, and prioritize appointments accordingly to improve healthcare efficiency and patient safety. 4. Aim and Objectives Aim: To design and implement a web-based Artificial Intelligence system that performs early cardiovascular risk stratification and intelligent appointment prioritization. Objectives: I) To develop and train machine learning models (Logistic Regression, Random Forest, and Gradient Boosting) for classifying cardiovascular patients into low, medium, and high-risk categories. II) To design and implement a web-based interface that allows healthcare personnel to input patient data and receive real-time risk predictions. III) To develop an appointment prioritization module that schedules patients based on predicted risk levels and evaluate the system using performance metrics such as accuracy, precision, recall, and F1-score. 5. Research Methodology Research Approach: A quantitative research approach will be used, involving supervised machine learning model development, system implementation, and performance evaluation. Tools / Technologies to be Used: * Python * Scikit-learn * Pandas and NumPy * XGBoost * Flask or Streamlit (Web Framework) * HTML, CSS, Bootstrap * MySQL / SQLite (Database) Dataset: * UCI Heart Disease Dataset * Additional cardiovascular datasets from Kaggle (if required) Evaluation Metrics: * Accuracy * Precision * Recall * F1-Score * Confusion Matrix * Basic scheduling efficiency analysis 6. Scope of the Study The study will focus on cardiovascular risk prediction using structured numerical patient data and the development of a web-based prototype system for appointment prioritization. It will not include real-time hospital integration, hardware implementation, or full clinical validation. The system will serve as an academic prototype for evaluation purposes. 7. Expected Outcomes * A trained cardiovascular risk stratification model * A functional web-based AI prototype system * Automated appointment prioritization based on risk levels * Performance evaluation report comparing different machine learning algorithms * Recommendations for integrating AI into hospital scheduling systems 8. References 1. World Health Organization (WHO). (2021). Cardiovascular diseases (CVDs) fact sheet. 2. Rajkomar, A., et al. (2018). Scalable and accurate deep learning for electronic health records. npj Digital Medicine. 3. Detrano, R., et al. (1989). International application of a new probability algorithm for coronary artery disease diagnosis. American Journal of Cardiology. 4. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine.