Extreme Logistic Regression: A Large Scale Learning Algorithm with Application to Prostate Cancer Mortality Prediction

Ngufor, Che (George Mason University) | Wojtusiak, Janusz (George Mason University) | Hooker, Andrea (George Mason University) | Oz, Talha (George Mason University) | Hadley, Jack (George Mason University)

AAAI Conferences 

With the recent popularity of electronic medical records, enormous amount of medical data is being generated every day at an exponential rate.Machine learning methods have been shown in many studies to be capable of producing automatic medical diagnostic models such as automated prognostic models. However, many powerful machine learning algorithms such as support vector machine (SVM), Random Forest (RF) or Kernel Logistic Regression (KLR) are unbearably slow for very large datasets. This makes their use in medical research limited to small to medium scale problems.This study is motivated by an ongoing research on prostate cancer mortality prediction for a national representative of US population where the SVM and RF took several hours or days to trainwhereas simple linear methods such as logistic regression or linear discriminant analysis take minutes or even seconds.Because, most real-world problems are non-linear, this paper presents a large scale algorithm enabling a recently proposed least squares extreme logistic regression to learn very large datasets. The algorithm is shown on a case study of mortality prediction for men diagnosed with early stage prostate cancer to provide very fast and more accurate result than standard statistical methods.

Duplicate Docs Excel Report

Title
None found

Similar Docs  Excel Report  more

TitleSimilaritySource
None found