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%.

Chickenpox is classified as a transmission disease, especially young ages from three months to 15 years old. This study clarifies the effect of the area, population, and the number of the health centers on the number of cases of chickenpox disease in the Rusafa district, Baghdad, Iraq. We use the Partial Linear Model (PLM) that divides the independent variables into two parts (parametric and non-parametric). Moreover, choosing the best model that represents data of the chickenpox disease, using criteria (R2, Bic, Aic) from six models. The results of the study show a positive relation between the number of cases and number of health centers.

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