Maleeha Qazi

Sparse classifiers for Automated HeartWall Motion Abnormality Detection

Coronary Heart Disease is the single leading cause of death world-wide, with lack of early diagnosis being a key contributory factor. This disease can be diagnosed by measuring and scoring regional motion of the heart wall in echocardiography images of the left ventricle (LV) of the heart. We describe a completely automated and robust technique that detects diseased hearts based on automatic detection and tracking of the endocardium and epicardium of the LV. We describe a novel feature selection technique based on mathematical programming that results in a robust hyperplane-based classifier. The classifier depends only on a small subset of numerical feature extracted from dualcontours tracked through time. We verify the robustness of our system on echocardiograms collected in routine clinical practice at one hospital, both with the standard crossvalidation analysis, and then on a held-out set of completely unseen echocardiography images.

An Insurance Recommendation System Using Bayesian Networks

In this paper we describe a deployed recommender system to predict insurance products for new and existing customers. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they were adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our system. Experimental results show advantages of using probabilistic graphical models over the widely used low-rank matrix factorization model for the insurance domain.