1 citations · 3 across the 5 of their papers we have counts for
5 papers
Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer Metastasis Using Real-World Clinical Data with AUC up to 0.9
Xia Jiang, Yijun Zhou, Alan Wells +1
Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,000 deaths each year in the US. That there are over 300,000 breast cancers newly dia…
Deep Learning to Predict Late-Onset Breast Cancer Metastasis: the Single Hyperparameter Grid Search (SHGS) Strategy for Meta Tuning Concerning Deep Feed-forward Neural Network
Yijun Zhou, Om Arora-Jain, Xia Jiang
While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedi…
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)
Chenyu Li, Xia Jiang
Introduction Data imbalance is one of the crucial issues in big data analysis with fewer labels. For example, in real-world healthcare data, spam detection labels, and financial fr…
iMedBot: A Web-based Intelligent Agent for Healthcare Related Prediction and Deep Learning
Chuhan Xu, Xia Jiang
Background: Breast cancer is a multifactorial disease, genetic and environmental factors will affect its incidence probability. Breast cancer metastasis is one of the main cause of…
Empirical Study of Overfitting in Deep FNN Prediction Models for Breast Cancer Metastasis
Chuhan Xu, Pablo Coen-Pirani, Xia Jiang
Overfitting is defined as the fact that the current model fits a specific data set perfectly, resulting in weakened generalization, and ultimately may affect the accuracy in predic…