International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Driven Approach for Heart Disease Risk Prediction and Personalized Intervention |
|---|---|
| ABSTRACT | CVDs remain a primary global mortality cause, requiring early, non-invasive diagnostic tools. This project utilizes liquid biopsy data—cfRNA, microRNAs, and extracellular vesicles—for early risk detection. We implemented and evaluated ML models, including Random Forest, XGBoost, and Multi-layer Perceptron, using AUC-ROC and precision-recall metrics. A MERN stack platform enables seamless patient registration, tracking, and visualization. The system integrates an AI chatbot to translate complex biomarkers into patient-friendly insights. By merging molecular diagnostics with machine learning, this solution facilitates proactive clinical monitoring. Our results demonstrate high accuracy in predicting early cardiovascular abnormalities. This platform bridges the gap between lab data and clinical application. |
| AUTHOR | PROF. RACHANA G SUNKAD, KEERTHI D C, BHUVANA N M, DARSHAN H J, SUHAS HABBU Assistant Professor, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India UG Students, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312127 |
| pdf/127_AI-Driven Approach for Heart Disease Risk Prediction and Personalized Intervention.pdf | |
| KEYWORDS | |
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