Advanced Machine Learning Techniques to Improve Genomic Data Accuracy for Precision Medicine

Precision diagnosis in healthcare relies heavily on the analysis of genomic data to tailor treatments to individual patients. However, achieving high sensitivity and specificity in genomic data analysis remains a challenge. This paper explores the integration of innovative machine learning approaches, including random forests, convolutional neural networks (CNNs), and transfer learning, to enhance diagnostic accuracy in precision medicine. Through the analysis of real -world genomic datasets, we demonstrate the efficacy of these techniques in improving sensitivity and specificity for precision diagnosis. Our findings indicate that CNNs and transfer learning models significantly outperform traditional methods, offering robust solutions for genomic data analysis. Additionally, we discuss the trade -offs between performance and interpretability, emphasizing the need for explainable AI techniques in future research. The insights gained from this study contribute to advancing precision diagnosis in genomics, with potential benefits for clinical applications.