Combination of Nanobiosensors and Machine Learning for Cancer Detection: A Comprehensive Review

As a result of the increasing incidence rate of cancer, early and accurate cancer diagnosis is of utmost importance. In recent years, scientists have paid increasing attention to emergent technologies such as nanobiosensors and machine learning, which are becoming increasingly popular over time. The development of nanobiosensors and the use of machine learning to analyze large amounts of data and detect hidden patterns can contribute to cancer diagnosis by improving precision and identifying molecular measurements. The purpose of this review article is to examine how these two technologies can be combined to improve the accuracy and speed of cancer diagnosis and to analyze recent developments in this area as well. Methods: Extensive searches were conducted in authoritative scientific databases such as PubMed and Scopus to collect data and scientific articles related to the topic. Selected articles have been published between ۲۰۱۰ and ۲۰۲۴ and have dealt with the combination of nanobiosensors and machine learning algorithms in cancer diagnosis. Nanobiosensors, machine learning, cancer diagnosis, molecular biomarkers, and cancer prediction were used as keywords. Results: Based on the findings of the literature review the use of nanobiosensors has been shown to be an effective means of detecting cancer biomarkers with a very high level of accuracy. Using nanobiosensors that are based on gold nanoparticles, for instance, it has been found that cancer cells can be detected even at the early stages by using DNA that is present in tumors. In this instance, the nanobiosensors are capable of working with small and dilute samples as they have very high levels of reactivity as well as a high surface-to-volume ratio, which is characteristic of nanoparticles. Specifically, machine learning plays a crucial role in the production of sensor data, as well as interpreting the results of that data. As a result of the data provided by nanobiosensors, a number of machine learning algorithms have been applied to analyze them, such as neural networks, support vector machines (SVM), and random forests. As an example, in a study that was conducted with neural networks, it was found that graphene nanobiosensors and neural networks were effective in detecting colon cancer with over ۹۵?curacy. In addition, the combination of machine learning with silicon nanoparticle-based sensors has been shown to improve diagnostic sensitivity and minimize human error, as has been shown in the case of prostate cancer diagnosis using this approach. The use of a deep learning algorithm has been proven to be useful for analyzing data collected from nanobiosensors based on RNA-based molecules in order to detect breast cancers more quickly. Conclusion: Nanobiosensors, which can be used in conjunction with machine learning in order to detect cancer, are an innovative and effective approach to cancer diagnosis, which can result in significant improvements in accuracy, sensitivity, and speed of diagnosis in the future. As a result of these technologies, not only are doctors able to identify cancer earlier, but they are also able to make better decisions by analyzing complex data and making informed decisions.

کلیدواژه ها:

Nanobiosensors ، Machine learning ، Cancer diagnosis ، Nanoparticles ، Biomarker