New Hybrid Algorithm for Human Cancer Diseases Classification | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 9, Volume 25, Issue 2, July 2016, Page 267-282 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2016.64116 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Hanaa Salem1; Gamal Attiya 2; Nawal El-Fishawy2
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| 1Communications & Computer Dept., Faculty of Engineering, Delta University, Egypt | ||||
| 2Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University | ||||
| Abstract | ||||
| "> Cancer disease, in any of its forms, represents a major cause of death worldwide. Hence, detecting the cancer disease earlier and classifying the different tumor types is of the greatest importance. Early diagnosis of various tumor types gives better treatment and minimization of toxicity on patients. Accordingly, creating methodologies that able to differentiate efficiently between cancer subtypes is essential. This paper presents a new hybrid methodology to classify Human cancer diseases based on the gene expression profiles. The proposed methodology combines both Information gain (IG) and Deep Genetic Algorithm (DGA). It first uses IG for feature selection, then uses Genetic Algorithm (GA) for feature reduction and finally uses Genetic Programming (GP) for cancer types’ classification. The proposed methodology is evaluated by classifying cancer diseases in seven cancer datasets and the results are compared with that obtained by the most recent approaches. | ||||
| References | ||||
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