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Analysis Study Research Paper | Computer Science and Information Technology | United States of America | Volume 13 Issue 6, June 2024 | Popularity: 5.3 / 10
Advanced Machine Learning Techniques to Improve Genomic Data Accuracy for Precision Medicine
Neha Dhaliwal
Abstract: 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.
Keywords: Machine Learning, Genomics, Precision Diagnosis, Random Forests, Convolutional Neural Networks, Transfer Learning, Explainable AI
Edition: Volume 13 Issue 6, June 2024
Pages: 579 - 584
DOI: https://www.doi.org/10.21275/SR24608014916
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