DOI : https://doi.org/10.18517/ijaseit.10.5.12969

The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq

Nabaa Naeem Mahdi (1), Haifa Taha Abed (2), Nazik J. Sadik (3)
(1) College of Management and Economics, University of Mustansiriyah, Iraq
(2) College of Management and Economics, University of Mustansiriyah, Iraq
(3) College of Management and Economics, University of Mustansiriyah, Iraq
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How to cite (IJASEIT) :
Mahdi, Nabaa Naeem, et al. “The Discriminant Analysis in the Evaluation of Cancers Diseases in Iraq”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, Oct. 2020, pp. 2170-6, doi:10.18517/ijaseit.10.5.12969.
Citation Format :
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%.

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