Abstract

Nanoelectronics has revolutionized the field of biomedical data science by providing advanced tools for data acquisition and processing. Recent advancements in transformer algorithms have opened new avenues for enhancing the analysis of biomedical data, which is often complex and high-dimensional. Traditional methods struggle with the high volume and intricacy of biomedical data, leading to suboptimal performance in disease diagnosis, prognosis, and personalized treatment strategies. There is a need for more robust algorithms that can effectively handle and interpret this data. This study introduces a novel approach leveraging transformer algorithms integrated with nanoelectronics-based sensors for improved biomedical data analysis. The methodology involves preprocessing data from nanoelectronic sensors, applying transformer models to extract meaningful patterns, and evaluating performance against conventional algorithms. The proposed method demonstrated a 25% improvement in diagnostic accuracy and a 30% reduction in processing time compared to traditional methods. The model achieved an accuracy of 92% in disease classification tasks and reduced false positives by 40%.