Optimized Clinical Data Classification For Heart Disease Using Bio-Inspired Algorithms

The research shows how effective it is to combine Bio-Inspired Optimization with Machine Learning for the accurate classification of heart disease. By moving from traditional manual diagnostics to an automated framework, the system meets the challenges of high dimensionality and non-linearity in clinical datasets. The use of search agents, found in Swarm Intelligence and Evolutionary Algorithms, played a crucial role in pinpointing the most predictive physiological markers. This approach reduces computational load while improving diagnostic sensitivity. It ensures that the predictive model works well across different patient groups, creating a solid base for early intervention in heart conditions.   The experimental results and comparisons show that the new system greatly outperforms traditional classifiers in terms of accuracy and reliability. The steady connection between the clinical data layer and the diagnostic dashboard allows for real-time monitoring, which is essential in emergencies. Additionally, visualizing risk patterns through an easy-to-navigate interface helps connect complex calculations with practical clinical use. By achieving high sensitivity and a low false-negative rate, the system lowers the chances of missed diagnoses, which is vital for any critical medical technology.   In the future, this optimized framework offers a scalable solution for today's digital healthcare and smart medical city infrastructures. Its modular design allows for easy addition of more clinical markers or adaptation to other chronic diseases. As healthcare shifts toward Precision Medicine and the Internet of Medical Things, such smart systems will be key in lowering mortality rates and enhancing patient care. This project highlights how nature-inspired computation can address significant challenges in cardiology and diagnostic informatics.

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