How has synthetic data contributed to improving breast cancer detection?
Reading time: 3 minutes
Breast cancer
It is important to know that breast cancer is the most common type of cancer among women in France, the European Union, and the United States. Although this disease was the leading cause of cancer death among women in 2018, the number of cases diagnosed each year has been decreasing since 2005. If breast cancer is detected at an early stage, the 5-year survival rate is 99%. Early detection of breast cancer therefore has a significant impact on reducing the mortality rate of the disease.
AI in the service of medicine
Currently, several artificial intelligence tools exist to help healthcare professionals accelerate diagnosis and facilitate treatment decisions. By combining genomic sequencing data with machine learning algorithms, it is possible to fight cancer.
Machine learning can aid in the detection, treatment, and prognosis of the disease, as well as in the development of personalized treatments. This approach leverages data from multiple patients to identify similarities and correlations between them and thus better understand the disease.
However, artificial intelligence is currently hampered by the limited amount of accessible data. So how can we enable AI to break through this barrier and reach the next stage of its evolution? To answer this question, we offer a use case using the “Breast Cancer Wisconsin (Diagnostic) – UCI Machine Learning Repository” database. This dataset aims to predict whether the tumor type is malignant or benign. We therefore decided to augment the training database of a classification artificial intelligence model using synthetic data.
The study
The comparison
Conclusion
The use of synthetic data has brought significant improvements in breast cancer detection through artificial intelligence. The performance of classification models has been enhanced, which therefore implies better predictions and a larger database for research. While preserving patient confidentiality, synthetic data thus opens new avenues for innovation in the fight against breast cancer. This promising approach then paves the way for new advances in the field of health.
