An efficient fuzzy logic and artificial intelligence based optimization strategy for bigdata healthcare system - Kumar, Ravi; Gokulakrishnan, S.; Kosuru, S. N. V. J. Devi; kumar, R.Praveen; Rajesh, Thota Radha (Edelweiss Applied Science and Technology, 2025)
Digital health has revolutionized patient care by integrating big data analytics for predictive diagnostics, personalized treatment, and real-time health monitoring. However, the rapid generation of healthcare data from IoT devices, electronic health records, and medical imaging poses challenges such as high dimensionality, noise, and real-time processing. Existing methodologies struggle to balance accuracy and efficiency, making them limited for real-time healthcare applications. This paper proposes an optimization technique for big data-based healthcare systems incorporating fuzzy logic and artificial intelligence for predictive decision-making. This framework considers IoT-collected health data, which in itself brings forth problems of high dimensionality, noise, and real-time analytics. An intelligent preprocessing stage encompasses noise reduction and data integration, providing consistency and reliability for the dataset, which uses a fuzzy logic system. For optimal feature selection, an advanced AI model combines Lion optimization with heap-based feature estimation, thus reducing dimensionality while conserving health-relevant information. The optimized features are classified using a Hybrid Golden Eagle-Self-constructing Neural Fuzzy (HGE-SNF) algorithm, which dynamically tunes the weights and biases toward optimal classification performance. This hybrid approach improves predictive accuracy in disease detection and patient management issues and enhances computational efficiency for real-time healthcare applications. Experimental results indicate that it performs better than traditional methods and has great potential to revolutionize big data analytics in health systems.