Name
Abstract | ||
|---|---|---|
The most important factors for the diagnosis of diabetes mellitus (DM) are age, body mass index (BMI) and blood glucose concentration. Diagnosis of DM by a doctor is complicated, because several factors are involved in the disease, and the diagnosis is subject to human error. A blood test does not provide enough information to make a correct diagnosis of the disease. A vector support machine (SVM) was implemented to predict the diagnosis of DM based on the factors mentioned in patients. The classes of the output variable are three: without diabetes, with a predisposition to diabetes and with diabetes. An SVM was obtained with an accuracy of 99.2% with Colombian patients and an accuracy of 65.6% with a data set of patients of a different ethnic background. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.procs.2020.03.065 | Procedia Computer Science |
Keywords | DocType | Volume |
Medical Diagnosis,Diabetes Mellitus,Medical Computing,Machine Learning,Vector Support Machines | Conference | 170 |
ISSN | Citations | PageRank |
1877-0509 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amelec Viloria | 1 | 4 | 6.81 |
| Yaneth Herazo-Beltran | 2 | 1 | 0.35 |
| Danelys Cabrera | 3 | 1 | 1.03 |
| Omar Bonerge Pineda Lezama | 4 | 1 | 12.86 |