Intelligent Healthcare Analytics using Evolutionary Optimization Techniques
The integration of intelligent healthcare analytics with evolutionary optimization techniques offers a transformative approach to medical decision-making and patient care. Healthcare systems generate vast amounts of heterogeneous data, including clinical records, diagnostic images, and real-time sensor outputs, which demand advanced computational strategies for meaningful interpretation. Evolutionary algorithms, inspired by natural selection, provide robust mechanisms for feature selection, predictive modeling, and optimization of complex healthcare processes. By leveraging these techniques, intelligent analytics can enhance disease prediction accuracy, optimize treatment planning, and support resource allocation in dynamic clinical environments. Furthermore, evolutionary optimization enables adaptive learning, ensuring models remain effective across diverse patient populations and evolving medical datasets. This synergy not only improves diagnostic precision but also fosters personalized medicine, reducing risks and improving patient outcomes. The proposed framework underscores the potential of evolutionary computation as a cornerstone in next-generation healthcare analytics, bridging data-driven insights with practical clinical applications.
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Dr. Jayashree Piri of KIIT Deemed to be University Bhubaneswar, Odisha
: Intelligent Healthcare Analytics using Evolutionary Optimization Techniques
The integration of intelligent healthcare analytics with evolutionary optimization techniques offers a transformative approach to medical decision-making and patient care. Healthcare systems generate vast amounts of heterogeneous data, including clinical records, diagnostic images, and real-time sensor outputs, which demand advanced computational strategies for meaningful interpretation. Evolutionary algorithms, inspired by natural selection, provide robust mechanisms for feature selection, predictive modeling, and optimization of complex healthcare processes. By leveraging these techniques, intelligent analytics can enhance disease prediction accuracy, optimize treatment planning, and support resource allocation in dynamic clinical environments. Furthermore, evolutionary optimization enables adaptive learning, ensuring models remain effective across diverse patient populations and evolving medical datasets. This synergy not only improves diagnostic precision but also fosters personalized medicine, reducing risks and improving patient outcomes. The proposed framework underscores the potential of evolutionary computation as a cornerstone in next-generation healthcare analytics, bridging data-driven insights with practical clinical applications.
Biography:
Dr. Jayashree Piri is currently working as an Associate Professor in the School of Computer Engineering at KIIT Deemed to be University, Bhubaneswar, Odisha. She completed her M.E. in Information Technology from Jadavpur University, Kolkata, West Bengal, in 2012. Dr. Piri received her Ph.D. from IIIT Bhubaneswar, Odisha. She has more than 14 years of teaching experience. She has published numerous research papers in peer-reviewed journals such as Computers in Biology and Medicine, IEEE Access, Mathematics, Algorithms, and Evolutionary Intelligence. Dr. Piri also holds several German and Indian patents. She has delivered presentations at various international conferences, seminars, and workshops. Her research interests include Evolutionary Computing, Pattern Recognition, Machine Learning, and Medical Data Analysis.
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Address:Associate Professor, School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Bhubaneswar, Orissa, India, 751024