An Impact of Artificial Intelligence Techniques by Using SMO Algorithm with Several Kernels in Privacy Preservation of Medical Data

Considering security issues, artificial intelligence plays a critical role in improving privacy when medical data is shared in healthcare services. Technological developments enable smooth data sharing between enterprises, governments, and private citizens. Heart disease, strokes, and heart failure are among the cardiovascular illnesses (CVDs) that are the focus of this research. The SMO algorithm that uses PolyKernel has the best accuracy rate, at 83.61%. RBFKernel has the lowest accuracy, at 67.68%, while Puk and NormalizedPolyKernel record the highest accuracy, ranging from 74 to 79%. With lowest mean error (0.16), low RMSE (0.4), ReMSE (37.54%), and RRSE (86.69%) values, PolyKernel also shows the highest precision (0.83) and recall (0.84). This work highlights PolyKernel’s excellent performance over other kernels and emphasises its effectiveness in SMO classification for CVDs. The study highlights how AI may improve data analysis while addressing privacy issues in healthcare environments. Healthcare providers can get precise diagnosis and data-driven insights while guaranteeing strong privacy protection by utilising AI approaches such as SMO with PolyKernel. These results support the continuous development of safe and effective medical data management systems, encouraging compliance and trust in data sharing procedures among various healthcare sectors.