Name
Title | ||
|---|---|---|
An Ophthalmologist's Tool for Predicting Deterioration in Patients with Accommodative Esotropia |
Abstract | ||
|---|---|---|
The work described in this paper applies machine learning techniques, to a database of accommodative esotropic patients. Accommodative esotropia is an eye disease that when left untreated leads to blindness. Patients whose muscles deteriorate most often need corrective surgery in order to prevent this, since less invasive methods of treatment tend to fail in these patients. It is often difficult for physicians to determine apriori which patients will deteriorate enough to require surgery. Using a learn and prune methodology, decision tree analysis of accommodative esotropic patients led to the discovery of two conjunctive variables that predicted deterioration. The use of these variables produced better predictions, and gave insight to domain experts. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ITNG.2013.114 | Information Technology: New Generations |
Keywords | Field | DocType |
corrective surgery,accommodative esotropia,eye disease,invasive method,accommodative esotropic patient,untreated lead,predicting deterioration,domain expert,better prediction,decision tree analysis,conjunctive variable,pediatrics,accuracy,data mining,learning artificial intelligence,support vector machines,surgery,decision trees,lenses | Eye disease,Decision tree,Computer science,Accommodative esotropia,Artificial intelligence,Optometry,Blindness,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-0-7695-4967-5 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Susan P. Imberman | 1 | 0 | 0.68 |
| Sarah Zelikovitz | 2 | 181 | 16.42 |
| Irene Ludwig | 3 | 0 | 0.68 |