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INTERNATIONAL CONGRESS OF

CancerGenetics

• Machine Learning in Combination with Nanobiosensors for Cancer Diagnosis

• Soroush Partovi Moghaddam,¹ Soheil Sadr,² Mahya Hashempour,³ Ashkan Hajjafari,⁴ Abbas Rahdar,^5,* Sadanand Pandey,⁶
  1. Department of Pathobiology, Faculty of Veterinary Medicine Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran
  3. Doctor of Veterinary Medicine Students, University of Tehran, Tehran, Iran
  4. Department of Pathobiology, Faculty of Veterinary Medicine Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
  5. Department of Physics, University of Zabol, Zabol, Iran
  6. Department of Chemistry, College of Natural Science, Yeungnam University, 280 Daehak‐Ro, Gyeongsan 38541, Korea School of Bioengineering and Food Technology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan 173229, Himachal Pradesh, India
  
  
  
• Introduction: In recent years, the use of advanced technologies such as machine learning (ML) and nanobiosensors in early cancer detection has attracted much attention. The purpose of this research is to investigate how to combine machine learning with nanobiosensors to improve the accuracy and speed of cancer diagnosis. Nanobiosensors with a high ability to detect biomolecules related to cancer can provide highly accurate data that are processed and interpreted using machine learning algorithms. This combination increases diagnostic sensitivity and specificity and has been introduced as a promising method for the rapid and non-invasive diagnosis of cancers. The purpose of this article is to evaluate the performance of this technology and suggest ways to improve it in the future.
• Methods: In this study, data related to scientific articles published between 2018 and 2023 were extracted from reliable databases such as PubMed, Scopus, and Google Scholar. Keywords used included "machine learning," "nanobiosensors," "cancer diagnosis," "artificial intelligence in medicine," and "nanostructured biomarkers". The criteria for selecting articles were to be relevant to the topic and provide detailed data on the application of nanobiosensors and machine learning in cancer diagnosis.
• Results: The results of the analysis show that the use of machine learning in combination with nanobiosensors has led to a significant improvement in the accuracy and speed of cancer diagnosis, such as colon cancer, lung cancer, breast cancer, melanoma, and hepatic cancer. For example, in one study, the use of nanobiosensors based on gold nanoparticles with neural network algorithms was able to detect breast cancer with 95% accuracy. Also, in another study that examined lung cancer, the use of graphene nanobiosensors along with Random Forest algorithms led to an increase in sensitivity up to 92%. This compound is especially useful in detecting cancer biomarkers that exist in very low concentrations in the blood and is especially important in non-invasive cancers and early stages of cancer. One of the studies also examined the application of deep learning algorithms in the processing of nanobiosensors data, which led to an increase in the accuracy of pancreatic cancer diagnosis to more than 90%. In this research, one of the main challenges is processing large amounts of data and optimizing algorithms for greater accuracy. The results show that the combination of machine learning and nanobiosensors has a high potential to provide fast and more accurate diagnostic methods.
• Conclusion: The use of machine learning and nanobiosensors as a new approach to a cancer diagnosis has a high potential to increase accuracy, speed, and efficiency in this field. This technology can create a revolution, especially in the early and non-invasive diagnosis of cancers. According to recent developments, it is expected that this compound will be more widely used in clinical settings in the future.
• Keywords: Machine learning, Nanobiosensors, Cancer diagnosis, Artificial intelligence, Biomarker