Sheenam Sheenam

Papers

Total Citations

H-Index

About

Dr. Sheenam Sheenam is a rising figure in computational healthcare, with a focused expertise in applying machine learning to cardiovascular disease analysis. Her most cited work, "An Empirical Study of Machine Learning Methods for Analyzing Cardiovascular Disease" (2023), confronts a critical challenge: the high cost and invasive risks of traditional diagnostic procedures like angiography. By systematically evaluating a range of data collection methods and machine learning algorithms, Sheenam’s research pioneers safer, more accessible, and cost-effective diagnostic alternatives. This study, already garnering 3 citations, lays essential groundwork for shifting cardiac care from reactive, high-risk interventions toward proactive, data-driven screening. Her contributions are particularly vital for resource-limited settings where expensive diagnostic tools are scarce. Sheenam’s work represents a significant step toward democratizing heart health, promising to reduce both financial burdens and patient risk through intelligent, non-invasive analysis. As her citation impact grows, she is establishing herself as a key voice in the intersection of artificial intelligence and preventive cardiology, with future work likely to expand these empirical frameworks into broader clinical applications.