Using public data to develop a model to predict PAD
Abstract In this analysis data from reliable public sources is analyzed. Moderning supervised machine learning techniques permit us to develop a method to predict who has Peripheral Arterial Disease (PAD) and assess the risk of death amongst these patients. As the data source is a robust database at the CDC website, that is representative of the population of United States of America, the mathematical model derived should better apply to those who reside in USA. This analysis
Functional Analysis of 600 patients with Peripheral Arterial Disease (PAD)
The article in its entirity is in RPUBS. at http://rpubs.com/abbas-ali/PAD. RPUBS allows me to publish my analysis directly from RStudio and saves me a lot of time. Abstract In this analysis we present analysis of 600 patients with peripheral arterial disease (PAD) or suspected of having PAD. Patients underwent a subjective assessment using a modified validated patient assessment tool the Vasc-QOL or King Questionairre, measurement of baseline and post walk toe and arm pressu