With the advancement of artificial intelligence, the risk of personal information breaches has become a significant concern, underscoring the importance of technologies that ensure data privacy. This study proposes an adaptive logistic regression method to enhance the performance of privacy-preserving machine learning using homomorphic encryption. The proposed technique simultaneously performs model training and training parameter optimization, enabling higher accuracy even when operating on encrypted data. Experimental results demonstrate that the adaptive homomorphic encryption logistic regression method achieves 55.92% higher prediction accuracy compared to models trained with random parameters. Although the proposed approach significantly mitigates the noise-induced performance degradation common in conventional homomorphic encryption methods, it requires longer training times owing to the overhead of multi-round optimization.