| Autor: |
Oladele, Tinuke Omolewa, Aro, Taye Oladele, Segun, Adebisi Samuel |
| Předmět: |
|
| Zdroj: |
Computing & Information Systems; Oct2018, Vol. 22 Issue 3, p18-26, 9p |
| Abstrakt: |
Purpose: This paper aims at predicting skin diseases using Neural Network, Decision Tree and Ensemble method based on majority voting. Design/Methodology/Approach: The study employed three selected machine learning algorithms (classifiers) to predict skin diseases in WEKA data mining tool environment. Dermatological dataset was used to evaluate the predictive system. The performance evaluation was done based on accuracy, precision, true positive, false positive and recall. Comparative analysis was conducted on the three data mining techniques. Findings: Each individual classifier and the ensemble method was put through training and testing phase using N-fold cross validation technique (N value was set to 10) and J48 gave accuracy of 93.9891% while Multi-layer Perceptron gave 96.9945%. The accuracy of 95.3552% was obtained in ensemble method using majority voting. Precision and recall rate of 0.94 for J48, 0.97 for precision and recall values for MLP and 0.95 for precision / recall values for ensemble method. Practical Implications: The paper provides a better skin disease predictive model with much higher accuracy and generalization that avoids both over-fitting and under-fitting by combining two machine learning algorithms via the majority voting method. The developed skin disease predictive system will help the medical personnel to effectively predict skin disease accurately. Originality/Value: Several works have been proposed by researchers in data mining for diagnosis or prediction of skin diseases, but this study's major concern is on the performance evaluation in term of over-fitting, under-fitting and generalization of individual base classifier and ensemble based approach in predicting skin diseases. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
