Breast Cancer Detection Using Nanoparticle Sensor with Machine Learning Algorithms

This research is mainly focused on the early detection of breast cancer in women by their urine samples with nanoparticle sensors. It detects certain enzymes and proteins that can be the main cause of cancer by machine learning algorithms, correlation analysis, and logical regression methods and created a web-based breast cancer prediction website using started nanoparticle urine analysis data, which contains the DNA barcode sequences that match with DNA of patient urine which is user friendly to use for every person. This study mainly focuses on non-invasive cancer. We analyze non-invasive cancer by using urine sample test data collected from the patients, utilizing advanced technology to access DNA signatures associated with breast cancer biomarkers. Our approach involves the barcode of nanoparticle sensors, which matches the urine samples. After the urine samples match, they are applied to the sensors, translated into digital data, and transmitted to a centralized system. Now, the centralized system collaborates with datasets derived from previous breast cancer cases. The algorithm now analyses the urine data and identifies the patterns. Then, it correlates with different stages of breast cancer.