Breast Cancer Survival Prediction using Machine Learning and Gene Expression Profiles
Santos, D.
Show abstract
Breast cancer is one of the most common cancers with a high mortality rate among women. It is also the most frequent cause of cancer death in this population, with an estimated 684,996 deaths for that year (15.5% of cancer deaths in women) (IARC, 2020). In Brazil, breast cancer is also the most common type of cancer in women from all regions, after non-melanoma skin cancer. Therefore, an accurate and reliable system is needed for the early diagnosis of this cancer. Machine learning explores the study and construction of algorithms that can learn from their mistakes and make predictions about data. The results here are excellent with an accuracy and precision level of around 79%.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- ChatGPT-Enhanced ROC Analysis (CERA): A Shiny Web Tool for Finding Optimal Cutoff in Biomarker Analysis 96%
- Multi- Stage Feature Selection (MSFS) Algorithm for UWB- Based Early Breast Cancer Size Prediction 96%
- Directed Bayesian Networks established functional differences between breast cancer subtypes 95%
Similar papers in this journal
- On the predictability of postoperative complications for cancer patients: a Portuguese cohort study 96%
- Prediction of Sepsis Mortality in ICU Patients Using Machine Learning Methods 95%
- Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths 94%
Similar papers in this journal
- Extensive In Silico Analysis of the Functional and Structural Consequences of SNPs in Human ARX Gene 95%
- EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction 94%
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 93%
Similar papers in this journal
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 97%
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 95%
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.