A Machine-Learning Based Nano-Biosensing Study on Cancer Diagnosis and IoT Applications


  • Kashish Bansal Student, Electrical Engineering, Indian Institute of Technology Indore
  • Nabeena Ameen Assistant professor(Sr grade) Department of Information Technology BS Abdur Rahman Crescent Institute of Science and Technology Chennai, India
  • Sumeet Mathur Assistant Professor, Department of Computer Science & Engineering, University of Waikato Joint Institute at Zhejiang University Hangzhou, China
  • Arjun Vinay Avadhani Research Scholar, Department of Computer Science Engineering, PES University Bengaluru, Karnataka
  • Satwinder Singh Student, Indian Institute of Management Amritsar, Punjab
  • Divya Chikati Assistant Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India


Nanostructures, Cancer Detection, Biosensor, Machine Learning


Cancer is a very big cause of death & an extremely costly illness to treat. The likelihood of a cure & survival rates increase with early cancer discovery, but sadly, most tumours are only discovered after they have spread to other parts of the body. Biosensors are tools created to identify a particular biological analyte by translating the intricate biological interactions into an electrical signal whose strength is related to the analyte's concentration. Nanotechnology with nanoparticles enhances & modifies the bio-recognition element portion to improve the bio-sensing phenomenon & makes it one of the hottest issues attracting the scientific fraternity. The primary goal of this study is to examine several nanostructures that have been applied to bio-sensing, & certain implementations in the areas of cancer diagnosis & IoT, & also a brief introduction to machine-learning-based bio-sensing. To categorise microarray data in this study, IOT & machine learning (ML) methods were applied. Two sets of data were used to generate them: one having 1,919 protein types & the other with 24,481 protein types for 97 individuals, 46 of whom had a reoccurring illness & 51 of whom did not. According to the study's findings, before feature reduction, logistic regression (LR) yielded the highest outcomes (90.23%) & also Random Forest yielded good outcomes (67.22%). Support Vector Machine had the best accuracy rates - 99.23% in both techniques in the first data & 87.87% in Random Logistic Regression (RLR) & 88.82% in LTE in the second data. To conclude, nanotechnology development surely helped biosensors to advance to new heights.


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How to Cite

Bansal, K. ., Ameen, N. ., Mathur, S. ., Avadhani, A. V. ., Singh, S. ., & Chikati, D. . (2023). A Machine-Learning Based Nano-Biosensing Study on Cancer Diagnosis and IoT Applications. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 323–328. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3476



Research Article