Name
Title | ||
|---|---|---|
An implementation of Feature ranking using Machine learning techniques for Diabetes disease prediction. |
Abstract | ||
|---|---|---|
Disease diagnosis is an application area where machine learning tools are providing successful results. Diabetes disease is one of the crucial factors of death all over the world. The availability of huge amounts of medical data leads to the need for powerful learning tools to help medical experts to diagnose diabetes disease. Machine learning methods are helpful in the diagnosis of diabetes disease, showing a reasonable level of efficiency. But these data are redundant and are noisy in nature which negatively affects the process of observing knowledge and useful pattern. Machine learning techniques have attracted a big attention to researchers to turn such data into useful knowledge. Further relevant data can be extracted from huge records using filter based feature selection methods. In our study, a comparative analysis is drawn between four different filter based feature selection methods (Chisquare method, Information gain method, Cluster Variation method and Correlation method) based on Diabetes disease. Three classifiers (RBF, IBK and JRip) were implemented to estimate the performance of the algorithms. The study revealed that filter based feature selection methods enhance the performance of learning algorithms in effective prediction and diagnosis of diabetes disease. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2905055.2905100 | ICTCS |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sushruta Mishra | 1 | 1 | 0.36 |
| Pamela Chaudhury | 2 | 1 | 0.36 |
| Brojo Kishore Mishra | 3 | 6 | 3.55 |
| Hrudaya K. Tripathy | 4 | 19 | 4.94 |