Name
Title | ||
|---|---|---|
Gender specific and Age dependent classification model for improved diagnosis in Parkinson's disease. |
Abstract | ||
|---|---|---|
Accurate diagnosis is crucial for preventing the progression of Parkinsonu0027s, as well as improving the quality of life with individuals with Parkinsonu0027s disease. In this paper, we develop a gender specific and age dependent classification method to diagnose the Parkinsonu0027s disease using the handwriting based measurements. The gender specific and age dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD=1.63) with the female specific classifier, and 79.55% (SD=1.58) with the old age dependent classifier was observed in comparison to 75.76% (SD=1.17) accuracy with the generalized classifier. Finally, combining the age and gender information proved to be encouraging in classification. We performed a rigorous analysis to observe the dominance of gender specific and age dependent features for Parkinsonu0027s detection and ranked them using the support vector machine(SVM) ranking method. Distinct set of features were observed to be dominating for higher classification accuracy in different category of classification. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1904.09651 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ujjwal Das Gupta | 1 | 37 | 6.59 |
| Hritik Bansal | 2 | 0 | 0.34 |
| Deepak Joshi | 3 | 6 | 1.55 |