InfoQ

Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques

Any supervised machine learning algorithm could work. For demonstration purposes, we have chosen a random forest with 100 trees, all trained up to a depth of ten levels and with a maximum of three ...
ascopubs.org

Using machine learning to predict mortality in older patients with cancer: Decision tree and random forest analyses from the ELCAPA and ONCODAGE prospective cohorts.

Prediction of treatment (tx)-induced fatigue in breast cancer (BC) patients (pts) using machine learning on genome-wide association (GWAS) data in the prospective CANTO cohort. This is an ASCO Meeting ...
insideHPC

TECH TIP: An Introduction to Random Forests

Random Forests Co-Developer Dr. Adele Cutler visited Salford Systems in San Diego, CA. While she was visiting, she gave the staff a few quick presentations and interviews related to the popular Random ...
Science Daily

Teaching computers to guide science: Machine learning method sees forests and trees

While it may be the era of supercomputers and 'big data,' without smart methods to mine all that data, it's only so much digital detritus. Now researchers have come up with a novel machine learning ...
JSTOR Daily

Machine-Learning Algorithms to Code Public Health Spending Accounts

Objectives:Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the ...
ascopubs.org

Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches

Our study used the linked SEER-Medicare data. The patient samples included fee-for-service Medicare beneficiaries diagnosed with lung cancer from 2010 to 2011 (development cohort) and 2012 to 2013 ...