The model tested using BN’s, ANN’s, SVM’s, DT’s and RF’s to classify patient data into those with cancer relapses and those without.This BNN model predicts the recurrence of breast cancer.In the end, the model correctly predicted all patients using feature selected data and BN’s..Even though this was a really accurate model, it had a really small dataset of only 86 patients.In another similar study, researchers made an ML model that tested using SVM’s, ANN’s and regression to classify patients into low risk and high-risk groups for cancer recurrence..The SVM model outperformed the other two and had an accuracy rate of 84%..This was groundbreaking, as it was significantly more accurate than pathologists.The model that predicts cancer survival ratesThis model took in a dataset of 162,500 records and 16 key features..Using features such as the size of the tumor and the age of the patient, the model created a classification model for if the patient survived or not..The model was tested using SVM’s, ANN’s and semi-supervised learning (SSL: a mix between supervised and unsupervised learning)..It found SSL’s to be the most successful with an accuracy rate of 71%.Another study used ANN’s to predict the survival rate of patients suffering from lung cancer..It had an accuracy rate of 83%..This study is considered largely accurate, though it did not take into account other death-related factors such as blood clots.What Does the Future of Cancer Prognosis Look Like?AI is set to change the medical industry in the coming decades — it wouldn’t make sense for pathology to not be disrupted too.Currently, ML models are still in the testing and experimentation phase for cancer prognoses..As datasets are getting larger and of higher quality, researchers are building increasingly accurate models.Here’s what a future cancer biopsy might look like:You perform clinical tests, either at a clinic or at home..Data is inputted into a pathological ML system..A few minutes later, you receive an email with a detailed report that has an accurate prediction about the development of your cancer.While you might not see AI doing the job of a pathologist today, you can expect ML to replace your local pathologist in the coming decades, and it’s pretty exciting!ML models still have a long way to go, most models still lack sufficient data and suffer from bias.. More details