TY  - JOUR
T1  - The Data Mining Reliability for Melanoma Disease Diagnosis
AU - M. Haglan, Hussein AU - Sh. Mahmoud, Akeel 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 20
SP  - 8591
EP  - 8597
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.8591.8597
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.8591.8597
KW  - Melanoma sisease
KW  -Decision Support (DS)
KW  -data mining
KW  -Genetic Algorithm (GA)
KW  -Backpropagation
Neural Network (Bp-NN)
KW  -beneficial
AB  - Data mining methods are the amount of actual data are used to study these data to forecast entire some data to support a decision-making in a problem-solving. A data mining is very beneficial to study any disease parameters to support the decision development and specify the disease and details. In the proposed present studies, using the real algorithms of data mining methods to support various healthcare fields and accepted a correct decision about the diagnosis of melanoma disease and specify the risk reasons for this disease to support decision process. In this study, a data-mining technique of melanoma disease forecast using a mixed scheme of Backpropagation-Neural Network (Bp-NN) and Genetic Algorithms (GA) has been introduced. According the outcomes, it has been seen that a mixed model forecast melanoma disease with nearly 95% accuracy. Additionally, the tested samples of entities share the same risk factors a symptom. Data mining depends on these symptoms and parameters to detect melanoma disease.
ER  -