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The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq

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@article{IJASEIT12969,
   author = {Nabaa Naeem Mahdi and Haifa Taha Abed and Nazik J. Sadik},
   title = {The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {2170--2176},
   keywords = {Iraq; discriminant linear function; ROC curve; wilks lambda.},
   abstract = {Cancer diseases are considered one of the most critical problems facing the world's countries, especially the State of Iraq. Many local and international reports indicated that the weapons used in wars and the accompanying nuclear and chemical radiation are among the most prominent reasons for the spread of cancerous diseases in Iraq. This study found that Gender has the highest discriminating power, whereas the Grade variable has the least discriminatory power. Similarly, Behavior has the highest discriminatory power, whereas the Government has the least biased power. It became clear that the third group (those with breast cancer) had the highest probability of the correct classification. The probability of correct classification reached 92%, followed by the second group with brain cancer, where the probability of correct classification was 64%. Finally, the first group with bladder cancer had the lowest probability of correct classification. We conclude that increasing the sample size has a significant impact on the correct classification of observations. The effects of these weapons were tremendously harmful to public health and the environment. Its effect persisted after many years, so three groups of cancer patients (bladder, brain, and breast cancer) were analyzed from 2012 to 2017 using a statistical method to analyze multivariate data. The results showed gender and the nature of the tumor (Behavior) have the highest discriminating power. The results were entirely satisfactory, as the discriminatory predictive capacity obtained a level of success of 72.2%.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12969},
   doi = {10.18517/ijaseit.10.5.12969}
}

EndNote

%A Mahdi, Nabaa Naeem
%A Abed, Haifa Taha
%A Sadik, Nazik J.
%D 2020
%T The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq
%B 2020
%9 Iraq; discriminant linear function; ROC curve; wilks lambda.
%! The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq
%K Iraq; discriminant linear function; ROC curve; wilks lambda.
%X Cancer diseases are considered one of the most critical problems facing the world's countries, especially the State of Iraq. Many local and international reports indicated that the weapons used in wars and the accompanying nuclear and chemical radiation are among the most prominent reasons for the spread of cancerous diseases in Iraq. This study found that Gender has the highest discriminating power, whereas the Grade variable has the least discriminatory power. Similarly, Behavior has the highest discriminatory power, whereas the Government has the least biased power. It became clear that the third group (those with breast cancer) had the highest probability of the correct classification. The probability of correct classification reached 92%, followed by the second group with brain cancer, where the probability of correct classification was 64%. Finally, the first group with bladder cancer had the lowest probability of correct classification. We conclude that increasing the sample size has a significant impact on the correct classification of observations. The effects of these weapons were tremendously harmful to public health and the environment. Its effect persisted after many years, so three groups of cancer patients (bladder, brain, and breast cancer) were analyzed from 2012 to 2017 using a statistical method to analyze multivariate data. The results showed gender and the nature of the tumor (Behavior) have the highest discriminating power. The results were entirely satisfactory, as the discriminatory predictive capacity obtained a level of success of 72.2%.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12969
%R doi:10.18517/ijaseit.10.5.12969
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 5
%@ 2088-5334

IEEE

Nabaa Naeem Mahdi,Haifa Taha Abed and Nazik J. Sadik,"The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 2170-2176, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.12969.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mahdi, Nabaa Naeem
AU  - Abed, Haifa Taha
AU  - Sadik, Nazik J.
PY  - 2020
TI  - The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 2170
EP  - 2176
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Iraq; discriminant linear function; ROC curve; wilks lambda.
N2  - Cancer diseases are considered one of the most critical problems facing the world's countries, especially the State of Iraq. Many local and international reports indicated that the weapons used in wars and the accompanying nuclear and chemical radiation are among the most prominent reasons for the spread of cancerous diseases in Iraq. This study found that Gender has the highest discriminating power, whereas the Grade variable has the least discriminatory power. Similarly, Behavior has the highest discriminatory power, whereas the Government has the least biased power. It became clear that the third group (those with breast cancer) had the highest probability of the correct classification. The probability of correct classification reached 92%, followed by the second group with brain cancer, where the probability of correct classification was 64%. Finally, the first group with bladder cancer had the lowest probability of correct classification. We conclude that increasing the sample size has a significant impact on the correct classification of observations. The effects of these weapons were tremendously harmful to public health and the environment. Its effect persisted after many years, so three groups of cancer patients (bladder, brain, and breast cancer) were analyzed from 2012 to 2017 using a statistical method to analyze multivariate data. The results showed gender and the nature of the tumor (Behavior) have the highest discriminating power. The results were entirely satisfactory, as the discriminatory predictive capacity obtained a level of success of 72.2%.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12969
DO  - 10.18517/ijaseit.10.5.12969

RefWorks

RT Journal Article
ID 12969
A1 Mahdi, Nabaa Naeem
A1 Abed, Haifa Taha
A1 Sadik, Nazik J.
T1 The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 2170
OP 2176
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Iraq; discriminant linear function; ROC curve; wilks lambda.
AB Cancer diseases are considered one of the most critical problems facing the world's countries, especially the State of Iraq. Many local and international reports indicated that the weapons used in wars and the accompanying nuclear and chemical radiation are among the most prominent reasons for the spread of cancerous diseases in Iraq. This study found that Gender has the highest discriminating power, whereas the Grade variable has the least discriminatory power. Similarly, Behavior has the highest discriminatory power, whereas the Government has the least biased power. It became clear that the third group (those with breast cancer) had the highest probability of the correct classification. The probability of correct classification reached 92%, followed by the second group with brain cancer, where the probability of correct classification was 64%. Finally, the first group with bladder cancer had the lowest probability of correct classification. We conclude that increasing the sample size has a significant impact on the correct classification of observations. The effects of these weapons were tremendously harmful to public health and the environment. Its effect persisted after many years, so three groups of cancer patients (bladder, brain, and breast cancer) were analyzed from 2012 to 2017 using a statistical method to analyze multivariate data. The results showed gender and the nature of the tumor (Behavior) have the highest discriminating power. The results were entirely satisfactory, as the discriminatory predictive capacity obtained a level of success of 72.2%.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12969
DO  - 10.18517/ijaseit.10.5.12969