Divya Kumari
Papers
1
Total Citations
4
H-Index
1
About
Divya Kumari is an emerging researcher in the intersection of artificial intelligence and healthcare, with a primary focus on applying reinforcement learning to medical diagnostics. Her most cited work, "Performance Evaluation of DQN, DDQN and Dueling DQN in Heart Disease Prediction" (2023), provides a rigorous comparative analysis of three advanced deep reinforcement learning algorithms—Deep Q-Networks (DQN), Double DQN, and Dueling DQN—for improving heart disease classification accuracy. This study addresses the critical global challenge of cardiovascular diagnostics by demonstrating how reinforcement learning can enhance predictive performance over traditional methods. Though early in her career, with this paper accumulating 4 citations, Kumari’s work signals a promising contribution to AI-driven medical decision support systems. Her research bridges the gap between cutting-edge machine learning techniques and real-world clinical needs, offering a pathway toward more reliable, automated disease detection. As the field increasingly turns to reinforcement learning for complex classification tasks, Kumari’s comparative evaluation provides foundational insights for future developments in intelligent healthcare solutions.
Research Focus
Key Achievements
Top Papers
- 1